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The aim of this study was to determine alterations of the metabolome in blood plasma in response to concentric-eccentric leg exercise performed at a simulated altitude of 3,500 m. To do so, we recruited 11 well-trained subjects and performed an untargeted metabolomics analysis of plasma samples obtained before, 20 min after as well as on day 8 after five sets of maximal, concentric-eccentric leg exercises that lasted 90 s each. We identified and annotated 115 metabolites through untargeted liquid chromatography-mass spectrometry metabolomics and used them to further calculate 20 sum/ratio of metabolites. A principal component analysis (PCA) revealed differences in-between the overall metabolome at rest and immediately after exercise. Interestingly, some systematic changes of relative metabolite concentrations still persisted on day 8 after exercise. The first two components of the PCA explained 34% of the relative concentrations of all identified metabolites analyzed together. A volcano plot indicates that 35 metabolites and two metabolite ratios were significantly changed directly after exercise, such as metabolites related to carbohydrate and TCA metabolism. Moreover, we observed alterations in the relative concentrations of amino acids (e.g., decreases of valine, leucine and increases in alanine) and purines (e.g., increases in hypoxanthine, xanthine and uric acid). In summary, high intensity concentric-eccentric exercise performed at simulated altitude systematically changed the blood metabolome in trained athletes directly after exercise and some relative metabolite concentrations were still changed on day 8. The importance of that persisting metabolic alterations on exercise performance should be studied further.

Mass spectrometry (MS)-based metabolomics approaches have been used for characterizing the metabolite content and composition of biological samples in drug discovery and development, as well as in metabolic engineering, and food and plant sciences applications. Here, we describe a protocol routinely used in our laboratory to conduct a metabolic profiling of small polar metabolites from biological samples. Metabolites can be extracted from each sample using a methanol-based single-phase extraction procedure. The combination of LC-based hydrophilic interaction liquid chromatography (HILIC) and a hybrid quadrupole-time of flight (Q-ToF) mass spectrometer allows the comprehensive analysis of small polar metabolites including sugars, phosphorylated compounds, purines and pyrimidines, nucleotides, nucleosides, acylcarnitines, carboxylic acids, hydrophilic vitamins and amino acids. Retention times and accurate masses of metabolites involved in key metabolic pathways are annotated for routine high-throughput screening in both untargeted and targeted metabolomics analyses.

Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association study (ExWAS) on absolute concentrations of 175 metabolites in 3294 individuals. To increase power, we imputed the identified variants into an additional 2211 genotyped individuals of CHRIS. In the resulting dataset of 5505 individuals, we identified 85 single-variant genetic associations, of which 39 have not been reported previously. Fifteen associations emerged at ten variants with >5-fold enrichment in CHRIS compared to non-Finnish Europeans reported in the gnomAD database. For example, the CHRIS-enriched ETFDH stop gain variant p.Trp286Ter (rs1235904433-hexanoylcarnitine) and the MCCC2 stop lost variant p.Ter564GlnextTer3 (rs751970792-carnitine) have been found in patients with glutaric acidemia type II and 3-methylcrotonylglycinuria, respectively, but the loci have not been associated with the respective metabolites in a genome-wide association study (GWAS) previously. We further identified three gene-trait associations, where multiple rare variants contribute to the signal. These results not only provide further evidence for previously described associations, but also describe novel genes and mechanisms for diseases and disease-related traits.

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Arrhythmogenic cardiomyopathy (ACM) is a genetic-based cardiac disease accompanied by severe ventricular arrhythmias and a progressive substitution of the myocardium with fibro-fatty tissue. ACM is often associated with sudden cardiac death. Due to the reduced penetrance and variable expressivity, the presence of a genetic defect is not conclusive, thus complicating the diagnosis of ACM. Recent studies on human induced pluripotent stem cells-derived cardiomyocytes (hiPSC-CMs) obtained from ACM individuals showed a dysregulated metabolic status, leading to the hypothesis that ACM pathology is characterized by an impairment in the energy metabolism. However, despite efforts having been made for the identification of ACM specific biomarkers, there is still a substantial lack of information regarding the whole metabolomic profile of ACM patients. The aim of the present study was to investigate the metabolic profiles of ACM patients compared to healthy controls (CTRLs). The targeted Biocrates AbsoluteIDQ® p180 assay was used on plasma samples. Our analysis showed that ACM patients have a different metabolome compared to CTRLs, and that the pathways mainly affected include tryptophan metabolism, arginine and proline metabolism and beta oxidation of fatty acids. Altogether, our data indicated that the plasma metabolomes of arrhythmogenic cardiomyopathy patients show signs of endothelium damage and impaired nitric oxide (NO), fat, and energy metabolism.

Volumetric absorptive microsampling (VAMS) is a novel approach that allows single-drop (10 μL) blood collection. Integration of VAMS with mass spectrometry (MS)-based untargeted metabolomics is an attractive solution for both human and animal studies. However, to boost the use of VAMS in metabolomics, key pre-analytical questions need to be addressed. Therefore, in this work, we integrated VAMS in a MS-based untargeted metabolomics workflow and investigated pre-analytical strategies such as sample extraction procedures and metabolome stability at different storage conditions. We first evaluated the best extraction procedure for the polar metabolome and found that the highest number and amount of metabolites were recovered upon extraction with acetonitrile/water (70:30). In contrast, basic conditions (pH 9) resulted in divergent metabolite profiles mainly resulting from the extraction of intracellular metabolites originating from red blood cells. In addition, the prolonged storage of blood samples at room temperature caused significant changes in metabolome composition, but once the VAMS devices were stored at - 80 °C, the metabolome remained stable for up to 6 months. The time used for drying the sample did also affect the metabolome. In fact, some metabolites were rapidly degraded or accumulated in the sample during the first 48 h at room temperature, indicating that a longer drying step will significantly change the concentration in the sample. Graphical abstract Volumetric absorptive microsampling (VAMS) is a novel technology that allows single-drop blood collection and, in combination with mass spectrometry (MS)-based untargeted metabolomics, represents an attractive solution for both human and animal studies. In this work, we integrated VAMS in a MS-based untargeted metabolomics workflow and investigated pre-analytical strategies such as sample extraction procedures and metabolome stability at different storage conditions. The latter revealed that prolonged storage of blood samples at room temperature caused significant changes in metabolome composition, but if VAMS devices were stored at - 80 °C, the metabolome remained stable for up to 6 months.

Metabolomics and lipidomics aim to profile the wide range of metabolites and lipids that are present in biological samples. recently, ion mobility spectrometry (IMs) has been used to support metabolomics and lipidomics applications to facilitate the separation and the identification of complex mixtures of analytes. IMs is a gas-phase electrophoretic technique that enables the separation of ions in the gas phase according to their charge, shape and size. occurring within milliseconds, IMs separation is compatible with modern mass spectrometry (Ms) operating with microsecond scan speeds. thus, the time required for acquiring IMs data does not affect the overall run time of traditional liquid chromatography (lc)-Ms-based metabolomics and lipidomics experiments. the addition of IMs to conventional lc-Ms-based metabolomics and lipidomics workflows has been shown to enhance peak capacity, spectral clarity and fragmentation specificity. Moreover, by enabling determination of a collision cross-section (ccs) value-a parameter related to the shape of ions-IMs can improve the accuracy of metabolite identification. In this protocol, we describe how to integrate traveling-wave ion mobility spectrometry (tWIMs) into traditional lc-Ms-based metabolomic and lipidomic workflows. In particular, we describe procedures for the following: tuning and calibrating a sYnapt High-Definition Ms (HDMs) system (Waters) specifically for metabolomics and lipidomics applications; extracting polar metabolites and lipids from brain samples; setting up appropriate chromatographic conditions; acquiring simultaneously m/z, retention time and ccs values for each analyte; processing and analyzing data using dedicated software solutions, such as progenesis QI (nonlinear Dynamics); and, finally, performing metabolite and lipid identification using ccs databases and tWIMs-derived fragmentation information.

Ion mobility (IM) spectrometry can separate gas-phase ions according to their charge, molecular shape, and size. In recent years, several IM technologies have been integrated with mass spectrometry (MS) and launched as commercially available instrumentation for metabolomics analysis. The addition of IM to MS-based metabolomics workflows provides an additional degree of separation to chromatography and MS resolving power, improving peak capacity and signal-to-noise ratio. Moreover, it makes possible to experimentally derive collision cross section (CCS), which can be used as an additional coordinate for metabolite identification, together with accurate mass and fragmentation information. The addition of CCS to current metabolome database would allow to filter and score molecules based on their CCS values, adding more confidence in the identification process during metabolomics experiments. In this chapter, we present procedures for the integration of travelling-wave (TW)-IM into traditional MS-based metabolomics workflows.

Iron is an essential co-factor for several metabolic processes, including the Krebs cycle and mitochondrial oxidative phosphorylation. Therefore, maintaining an appropriate iron balance is essential to ensure sufficient energy production and to avoid excessive reactive oxygen species formation. Iron overload impairs mitochondrial fitness, however, little is known about the associated metabolic changes. Here we aimed to characterize the metabolic signature triggered by dietary iron overload over time in a mouse model, where mice received either a standard or a high-iron diet. Metabolic profiling was assessed in blood, plasma and liver tissue. Peripheral blood was collected by means of volumetric absorptive microsampling (VAMS). Extracted blood and tissue metabolites were analyzed by liquid chromatography combined to high resolution mass spectrometry. Upon dietary iron loading we found increased glucose, aspartic acid and 2-/3-hydroxybutyric acid levels but low lactate and malate levels in peripheral blood and plasma, pointing to a re-programming of glucose homeostasis and the Krebs cycle. Further, iron loading resulted in the stimulation of the urea cycle in the liver. In addition, oxidative stress was enhanced in circulation and coincided with increased liver glutathione and systemic cysteine synthesis. Overall, iron supplementation affected several central metabolic circuits over time. Hence, in vivo investigation of metabolic signatures represents a novel and useful tool for getting deeper insights into iron-dependent regulatory circuits and for monitoring of patients with primary and secondary iron overload, and those ones receiving iron supplementation therapy.

Mass spectrometry (MS)-based lipidomics and metabolomics approaches have been used to discover new diagnostic and therapeutic targets of neurodegenerative disorders. Here, we describe a protocol to conduct an integrated metabolomics and lipidomics profiling of postmortem brains of frozen tissue samples from clinically characterized patients and age-matched controls. Metabolites and lipids can be extracted from each brain tissue sample, using a biphasic liquid/liquid extraction method. An unbiased liquid chromatography MS-based lipidomics and metabolomics workflows allows to screen for the content and composition of lipids and polar metabolites for each brain tissue. Data processing and statistical analysis are then used to compare the molecular content of all the samples, grouping them into cluster based on molecular similarities. The final results highlight classes of metabolites and biochemical pathways that are altered in brain samples from diseased brains compared to those from healthy subjects, helping to generate novel hypotheses on their mechanistic and functional significance.

Background Plasma and serum are the most widely used matrices in clinical studies. However, some variability in absolute concentrations of metabolites are likely to be observed in these collection tubes matrices. Methods We analyzed 189 metabolites using the same protocol for quantitative targeted metabolomics (LC-MS/MS AbsoluteIDQ p180 Kit Biocrates) in three types of samples, serum, plasma EDTA and citrate, of 80 subjects from the Cooperative Health Research In South Tyrol cohort (40 healthy elderly and 40 healthy young). Results The concentration levels were higher in serum than citrate and EDTA, in particular for amino acids and biogenic amines. The average Pearson's correlation coefficients were however always higher than 0.7 for these two classes of metabolites. We could also demonstrate that blank EDTA vacutainer tubes contain a significant amount of sarcosine. Finally, we compared the metabolome of young people against elderly subjects and found that the highest number of metabolites significantly changing with age was detected in serum. Conclusion Serum samples provide higher sensitivity for biomarker discovery studies. Due to the presence of spurious amount of sarcosine in vacutainer EDTA tubes, plasma EDTA is not suitable for studies requiring accurate quantification of sarcosine.

Platelets (PLTs) deteriorate over time when stored within blood banks through a biological process known as PLT storage lesion (PSL). Here, we describe the refinement of the biochemical model of PLT metabolism, iAT-PLT-636, and its application to describe and investigate changes in metabolism during PLT storage. Changes in extracellular acetate and citrate were measured in buffy coat and apheresis PLT units over 10 days of storage in the PLT additive solution T-Sol. Metabolic network analysis of these data was performed alongside our prior metabolomics data to describe the metabolism of fresh (days 1–3), intermediate (days 4–6), and expired (days 7–10) PLTs. Changes in metabolism were studied by comparing metabolic model flux predictions of iAT-PLT-636 between stages and between collection methods. Extracellular acetate and glucose contribute most to central carbon metabolism in PLTs. The anticoagulant citrate is metabolized in apheresis-stored PLTs and is converted into aconitate and, to a lesser degree, malate. The consumption of nutrients changes during storage and reflects altered PLT activation profiles following their collection. Irrespective of the collection method, a slowdown in oxidative phosphorylation takes place, consistent with mitochondrial dysfunction during PSL. Finally, the main contributors to intracellular ammonium and NADPH are highlighted. Future optimization of flux through these pathways provides opportunities to address intracellular pH changes and reactive oxygen species, which are both of importance to PSL. The metabolic models provide descriptions of PLT metabolism at steady state and represent a platform for future PLT metabolic research.

The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems., This work was supported by the National Heart Lung and Blood Institute (R43HL123074 and R43HL127843), the European Research Council (232816), and the U.S. Department of Energy (DE-SC0008701)., Ritrýnt tímarit

The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model (Q10) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q10 coefficient of 2.89 ± 1.03, within the expected range of 2–3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism.

Background and objectives Metabolomics studies have revealed transition points in metabolic signatures of red cells during storage in SAGM, whose clinical significance is unclear. We set out to investigate whether these transition points occur independent of storage media and define differences in the metabolism of red cells in additive solutions. Materials and methods Red cell concentrates were stored in SAGM, AS-1, AS-3 or PAGGSM, and sampled fourteen times spanning Day 1-46. Following quality control, the samples were split into extracellular and intracellular aliquots. These were analysed with ultra-high-performance liquid chromatography coupled to mass spectrometry analysis affording quantitative metabolic profiles of both intra- and extracellular red cell metabolites. Results Differences were observed in glycolysis, purine salvage, glutathione synthesis and citrate metabolism on account of the storage solutions. Donor variability however hindered the accurate characterization of metabolic transition time-points. Intracellular citrate concentrations were increased in red cells stored in AS-3 and PAGGSM media. The metabolism of citrate in red cells in SAGM was subsequently confirmed using 13 C citrate isotope labelling and shown to originate from citrate anticoagulant. Conclusion Metabolic signatures that discriminate between 'fresh' and 'old' stored red cells are dependent upon additive solutions. Specifically, the incorporation and metabolism of citrate in additive solutions with lower chloride ion concentration is altered and impacts glycolysis.

BACKGROUND: Red blood cells (RBCs) are routinely stored and transfused worldwide. Recently, metabolomics have shown that RBCs experience a three-phase metabolic decay process during storage, resulting in the definition of three distinct metabolic phenotypes, occurring between Days 1 and 10, 11 and 17, and 18 and 46. Here we use metabolomics and stable isotope labeling analysis to study adenine metabolism in RBCs. STUDY DESIGN AND METHODS: A total of 6 units were prepared in SAGM or modified additive solutions (ASs) containing 15N5-adenine. Three of them were spiked with 15N5-adenine on Days 10, 14, and 17 during storage. Each unit was sampled 10 times spanning Day 1 to Day 32. At each time point metabolic profiling was performed. RESULTS: We increased adenine concentration in the AS and we pulsed the adenine concentration during storage and found that in both cases the RBCs' main metabolic pathways were not affected. Our data clearly show that RBCs cannot consume adenine after 18 days of storage, even if it is still present in the storage solution. However, increased levels of adenine influenced S-adenosylmethionine metabolism. CONCLUSION: In this work, we have studied in detail the metabolic fate of adenine during RBC storage in SAGM. Adenine is one of the main substrates used by RBCs, but the metabolic shift observed during storage is not caused by an absence of adenine later in storage. The rate of adenine consumption strongly correlated with duration of storage but not with the amount of adenine present in the AS.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. Here we present an ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) method for extracellular measurements of known and unexpected metabolites in parallel. The method was developed by testing 86 metabolites, including amino acids, organic acids, sugars, purines, pyrimidines, vitamins, and nucleosides, that can be resolved by combining chromatographic and m/z dimensions. Subsequently, a targeted quantitative method was developed for 80 metabolites. The presented method combines a UPLC approach using hydrophilic interaction liquid chromatography (HILIC) and MS detection achieved by a hybrid quadrupole-time of flight (Q-ToF) mass spectrometer. The optimal setup was achieved by evaluating reproducibility and repeatability of the analytical platforms using pooled quality control samples to minimize the drift in instrumental performance over time. Then, the method was validated by analyzing extracellular metabolites from acute lymphoblastic leukemia cell lines (MOLT-4 and CCRF-CEM) treated with direct (A-769662) and indirect (AICAR) AMP activated kinase (AMPK) activators, monitoring uptake and secretion of the targeted compound over time. This analysis pointed towards a perturbed purine and pyrimidine catabolism upon AICAR treatment. Our data suggest that the method presented can be used for qualitative and quantitative analysis of extracellular metabolites and it is suitable for routine applications such as in vitro drug screening. info:eu-repo/grantAgreement/EC/FP7/232816

The high chemical complexity of the lipidome is one of the major challenges in lipidomics research. Ion-mobility spectrometry (IMS), a gas-phase electrophoretic technique, makes possible the separation of ions in the gas phase according to their charge, shape, and size. IMS can be combined with mass spectrometry (MS), adding three major benefits to traditional lipidomic approaches. First, IMS-MS allows the determination of the collision cross section (CCS), a physicochemical measure related to the conformational structure of lipid ions. The CCS is used to improve the confidence of lipid identification. Second, IMS-MS provides a new set of hybrid fragmentation experiments. These experiments, which combine collision-induced dissociation with ion-mobility separation, improve the specificity of MS/MS-based approaches. Third, IMS-MS improves the peak capacity and signal-to-noise ratio of traditional analytical approaches. In doing so, it allows the separation of complex lipid extracts from interfering isobaric species. Developing in parallel with advances in instrumentation, informatics solutions enable analysts to process and exploit IMS-MS data for qualitative and quantitative applications. Here we review the current approaches for lipidomics research based on IMS-MS, including liquid chromatography-MS and direct-MS analyses of "shotgun" lipidomics and MS imaging.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. Despite recent advances in analytical and computational chemistry, lipid identification remains a significant challenge in lipidomics. Ion-mobility spectrometry provides an accurate measure of the molecules' rotationally averaged collision cross-section (CCS) in the gas phase and is thus related to ionic shape. Here, we investigate the use of CCS as a highly specific molecular descriptor for identifying lipids in biological samples. Using traveling wave ion mobility mass spectrometry (MS), we measured the CCS values of over 200 lipids within multiple chemical classes. CCS values derived from ion mobility were not affected by instrument settings or chromatographic conditions, and they were highly reproducible on instruments located in independent laboratories (interlaboratory RSD < 3% for 98% of molecules). CCS values were used as additional molecular descriptors to identify brain lipids using a variety of traditional lipidomic approaches. The addition of CCS improved the reproducibility of analysis in a liquid chromatography-MS workflow and maximized the separation of isobaric species and the signal-to-noise ratio in direct-MS analyses (e.g., "shotgun" lipidomics and MS imaging). These results indicate that adding CCS to databases and lipidomics workflows increases the specificity and selectivity of analysis, thus improving the confidence in lipid identification compared to traditional analytical approaches. The CCS/accurate-mass database described here is made publicly available. NIH/GM087714 eu-repo/grantAgreement/EC/FP7/232816 Alzheimer's Association/NIRG-11-203674

Background: Platelet concentrates (PCs) can be prepared using three methods: platelet (PLT)-rich plasma, apheresis, and buffy coat. The aim of this study was to obtain a comprehensive data set that describes metabolism of buffy coat-derived PLTs during storage and to compare it with a previously published parallel data set obtained for apheresis-derived PLTs. Study Design and Methods: During storage we measured more than 150 variables in 8 PLT units, prepared by the buffy coat method. Samples were collected at seven different time points resulting in a data set containing more than 8000 measurements. This data set was obtained by combining a series of standard quality control assays to monitor the quality of stored PLTs and a deep coverage metabolomics study using liquid chromatography coupled with mass spectrometry. Results: Stored PLTs showed a distinct metabolic transition occurring 4days after their collection. The transition was evident in PLT produced by both production methods. Apheresis-derived PLTs showed a clearer phenotype of PLT activation during early days of storage. The activated phenotype of apheresis PLTs was accompanied by a higher metabolic activity, especially related to glycolysis and the tricarboxylic acid cycle. Moreover, the extent of the activation differed between bags resulting in interbag variability in the storage lesion of apheresis-prepared PLTs. This may be related to donor-related polymorphism. Conclusion: This study demonstrated two discrete metabolic phenotypes in stored PLTs prepared with both apheresis and buffy coat methods. PLT activation occurs during the first metabolic phenotype and might lead to a low reproducibility of the apheresis PCs.

Ion mobility spectrometry is a gas-phase electrophoretic technique that separates ions according to their charge, shape and size [1]. The separation occurs within milliseconds, making it compatible with LC and MS. Ion mobility spectrometry can be integrated into current workflows for metabolomics, improving peak capacity and separation of the metabolome, when compared with conventional analyses without IMS. Ion mobility-derived information provides an orthogonal physicochemical parameter, the collision cross-section, which is related to the shape of the metabolite. When used in addition to retention time and accurate mass, collision cross-section values can enhance the specificity of metabolite identification. In this chapter, we review the use of ion mobility MS for metabolomics applications.

Most gut bacteria are obligate anaerobes and are important for human health. However, little mechanistic insight is available on the health benefits of specific anaerobic gut bacteria. A main obstacle in generating such knowledge is the lack of simple and robust coculturing methods for anaerobic bacteria and oxygen-requiring human cells. Here, we describe the development of a coculture system for intestinal Caco-2 cells and an anaerobic symbiont, Faecalibacterium prausnitzii, making use of 50 mL culture tubes. F. prausnitzii was grown in 40 mL YCFAG-agar with glass-adhered Caco-2 cells placed on top in 10 mL DMEM medium. Grown for 18–36 h in a humidified incubator at 37 °C and 5% CO2, coverslip-attached Caco-2 cells promoted growth and metabolism of F. prausnitzii, while F. prausnitzii suppressed inflammation and oxidative stress in Caco-2 cells. F. prausnitzii did not compromise Caco-2 cell viability. Exogenously added porcine mucin also promoted growth of F. prausnitzii, suggesting that it may be part of the mechanism of Caco-2-stimulated growth of F. prausnitzii. This ‘Human oxygen-Bacteria anaerobic‘ (HoxBan) coculturing system uniquely establishes host-microbe mutualism of a beneficial anaerobic gut microbe in vitro and principally allows the analysis of host-microbe interactions of pure and mixed cultures of bacteria and human cells.

Twenty-eight sponge specimens were collected at a shallow water hydrothermal vent site north of Iceland. Extracts were prepared and tested in vitro for cytotoxic activity, and eight of them were shown to be cytotoxic. A mass spectrometry (MS)-based metabolomics approach was used to determine the chemical composition of the extracts. This analysis highlighted clear differences in the metabolomes of three sponge specimens, and all of them were identified as Haliclona (Rhizoniera) rosea (Bowerbank, 1866). Therefore, these specimens were selected for further investigation. Haliclona rosea metabolomes contained a class of potential key compounds, the 3-alkyl pyridine alkaloids (3-APA) responsible for the cytotoxic activity of the fractions. Several 3-APA compounds were tentatively identified including haliclamines, cyclostellettamines, viscosalines and viscosamines. Among these compounds, cyclostellettamine P was tentatively identified for the first time by using ion mobility MS in time-aligned parallel (TAP) fragmentation mode. In this work, we show the potential of applying metabolomics strategies and in particular the utility of coupling ion mobility with MS for the molecular characterization of sponge specimens., The authors would like to thank Erlendur Bogason for his assistance in sample collection as well as being the "protector" of Strytan, the hydrothermal vent site. This project was funded by the Eimskip University Fund, The University of Iceland Research Fund, and the Icelandic Research Fund (grant no.: 110403021).

The human gut microbiota plays a central role in human well-being and disease. In this study, we present an integrated, iterative approach of computational modeling, in vitro experiments, metabolomics, and genomic analysis to accelerate the identification of metabolic capabilities for poorly characterized (anaerobic) microorganisms. We demonstrate this approach for the beneficial human gut microbe Faecalibacterium prausnitzii strain A2-165. We generated an automated draft reconstruction, which we curated against the limited biochemical data. This reconstruction modeling was used to develop in silico and in vitro a chemically defined medium (CDM), which was validated experimentally. Subsequent metabolomic analysis of the spent medium for growth on CDM was performed. We refined our metabolic reconstruction according to in vitro observed metabolite consumption and secretion and propose improvements to the current genome annotation of F. prausnitzii A2-165. We then used the reconstruction to systematically characterize its metabolic properties. Novel carbon source utilization capabilities and inabilities were predicted based on metabolic modeling and validated experimentally. This study resulted in a functional metabolic map of F. prausnitzii , which is available for further applications. The presented workflow can be readily extended to other poorly characterized and uncharacterized organisms to yield novel biochemical insights about the target organism.

Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used to investigate metabolic alternations in human diseases. An expression of the altered metabolic pathway utilization is the selection of metabolites consumed and released by cells. However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models remain underdeveloped compared to methods for other omics data. Herein, we describe a workflow for such an integrative analysis emphasizing on extracellular metabolomics data. We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how our methods can reveal differences in cell metabolism. Our models explain metabolite uptake and secretion by predicting a more glycolytic phenotype for the CCRF-CEM model and a more oxidative phenotype for the Molt-4 model, which was supported by our experimental data. Gene expression analysis revealed altered expression of gene products at key regulatory steps in those central metabolic pathways, and literature query emphasized the role of these genes in cancer metabolism. Moreover, in silico gene knock-outs identified unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model. Thus, our workflow is well-suited to the characterization of cellular metabolic traits based on extracellular metabolomic data, and it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context. Electronic supplementary material The online version of this article (doi:10.1007/s11306-014-0721-3) contains supplementary material, which is available to authorized users.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. Metabolomics is a rapidly evolving analytical approach in life and health sciences. The structural elucidation of the metabolites of interest remains a major analytical challenge in the metabolomics workflow. Here, we investigate the use of ion mobility as a tool to aid metabolite identification. Ion mobility allows for the measurement of the rotationally averaged collision cross-section (CCS), which gives information about the ionic shape of a molecule in the gas phase. We measured the CCSs of 125 common metabolites using traveling-wave ion mobility-mass spectrometry (TW-IM-MS). CCS measurements were highly reproducible on instruments located in three independent laboratories (RSD < 5% for 99%). We also determined the reproducibility of CCS measurements in various biological matrixes including urine, plasma, platelets, and red blood cells using ultra performance liquid chromatography (UPLC) coupled with TW-IM-MS. The mean RSD was < 2% for 97% of the CCS values, compared to 80% of retention times. Finally, as proof of concept, we used UPLC-TW-IM-MS to compare the cellular metabolome of epithelial and mesenchymal cells, an in vitro model used to study cancer development. Experimentally determined and computationally derived CCS values were used as orthogonal analytical parameters in combination with retention time and accurate mass information to confirm the identity of key metabolites potentially involved in cancer. Thus, our results indicate that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches. info:eu-repo/grantAgreement/EC/FP7/232816 NIH/1R01GM087714 NIRG-11-203674

BackgroundPlatelet (PLT) concentrates are routinely stored for 5 to 7 days. During storage they exhibit what has been termed PLT storage lesion (PSL), which is evident by a loss of hemostatic function when transfused into patients. The overall goal of this study was to obtain a comprehensive data set describing PLT metabolism during storage.Study Design and MethodsThe experimental approach adopted to achieve this goal combined a series of standard assays to monitor the quality of stored PLTs and a deep-coverage metabolomics study using liquid chromatography coupled with mass spectrometry performed on both the extracellular and the intracellular environments. During storage we measured 174 different variables in 6 PLT units, collected by apheresis. Samples were collected at eight different time points resulting in a data set containing more than 8000 measurements.ResultsStored PLTs did not undergo a monotonic decay, but experienced systematic changes in metabolism reflected in three discrete metabolic phenotypes: The first (Days 0-3) was associated with active glycolysis, pentose phosphate pathway, and glutathione metabolism and down regulation of tricarboxylic acid (TCA) cycle. The second (Days 4-6) was associated with a more active TCA cycle as well as increased purine metabolism. A third metabolic phenotype of less clinical relevance (Days 7-10) was associated with a faster decay of cellular metabolism.ConclusionPSL is not associated with a linear decay of metabolism, but rather with successive metabolic shifts. These findings may give new insight into the mechanisms underlying PSL and encourage the deployment of systems biology methods to PSL

An extraction method for intracellular metabolite profiling should ideally be able to recover the broadest possible range of metabolites present in a sample. However, the development of such methods is hampered by the diversity of the physico-chemical properties of metabolites as well as by the specific characteristics of samples and cells. In this study, we report the optimization of an UPLC-MS method for the metabolite analysis of platelet samples. The optimal analytical protocol was determined by testing seven different extraction methods as well as by employing two different LC-MS methods, in which the metabolites were separated by using hydrophilic interaction liquid chromatography (HILIC) and reversed phase liquid chromatography (RPLC). The optimal conditions were selected using the coverage of the platelets' metabolome, the response of the identified metabolites, the reproducibility of the analytical method, and the time of the analysis as main evaluation criteria. Our results show that methanol-water (7:3) extraction coupled with HILIC-MS method provides the best compromise, allowing identification of 107 metabolites in a platelet cell extract sample, 91% of them with a RSD% lower than 20. A higher number of metabolites could be detected when analyzing the platelet samples with two different LC-MS methods or when using complementary extraction methods in parallel. © 2012 Elsevier B.V