Open AccessArticle
Impact of Exercise and Aging on Rat Urine and Blood Metabolome. An LC-MS Based Metabolomics Longitudinal Study
Metabolites 2017, 7(1), 10; doi:10.3390/metabo7010010 -
Abstract
Aging is an inevitable condition leading to health deterioration and death. Regular physical exercise can moderate the metabolic phenotype changes of aging. However, only a small number of metabolomics-based studies provide data on the effect of exercise along with aging. Here, urine and
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Aging is an inevitable condition leading to health deterioration and death. Regular physical exercise can moderate the metabolic phenotype changes of aging. However, only a small number of metabolomics-based studies provide data on the effect of exercise along with aging. Here, urine and whole blood samples from Wistar rats were analyzed in a longitudinal study to explore metabolic alterations due to exercise and aging. The study comprised three different programs of exercises, including a life-long protocol which started at the age of 5 months and ended at the age of 21 months. An acute exercise session was also evaluated. Urine and whole blood samples were collected at different time points and were analyzed by LC-MS/MS (Liquid Chromatography–tandem Mass Spectrometry). Based on their metabolic profiles, samples from trained and sedentary rats were differentiated. The impact on the metabolome was found to depend on the length of exercise period with acute exercise also showing significant changes. Metabolic alterations due to aging were equally pronounced in sedentary and trained rats in both urine and blood analyzed samples. Full article
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Open AccessArticle
The Role of Sarcosine, Uracil, and Kynurenic Acid Metabolism in Urine for Diagnosis and Progression Monitoring of Prostate Cancer
Metabolites 2017, 7(1), 9; doi:10.3390/metabo7010009 -
Abstract
The aim of this pilot study is to evaluate sarcosine, uracil, and kynurenic acid in urine as potential biomarkers in prostate cancer detection and progression monitoring. Sarcosine, uracil, and kynurenic acid were measured in urine samples of 32 prostate cancer patients prior to
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The aim of this pilot study is to evaluate sarcosine, uracil, and kynurenic acid in urine as potential biomarkers in prostate cancer detection and progression monitoring. Sarcosine, uracil, and kynurenic acid were measured in urine samples of 32 prostate cancer patients prior to radical prostatectomy, 101 patients with increased prostate-specific antigen prior to ultrasonographically-guided prostatic biopsy collected before and after prostatic massage, and 15 healthy volunteers (controls). The results were related to histopathologic data, Gleason score, and PSA (Prostate Specific Antigen). Metabolites were measured after analysis of urine samples with Ultra-High Performance Liquid Chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) instrumentation. Multivariate, nonparametric statistical tests including receiver operating characteristics analyses, one-way analysis of variance (Kruskal–Wallis test), parametric statistical analysis, and Pearson correlation, were performed to evaluate diagnostic performance. Decreased median sarcosine and kynurenic acid and increased uracil concentrations were observed for patients with prostate cancer compared to participants without malignancy. Results showed that there was no correlation between the concentration of the studied metabolites and the cancer grade (Gleason score <7 vs. ≥7) and the age of the patients. Evaluation of biomarkers by ROC (Receiving Operating Characteristics) curve analysis showed that differentiation of prostate cancer patients from participants without malignancy was not enhanced by sarcosine or uracil levels in urine. In contrast to total PSA values, kynurenic acid was found a promising biomarker for the detection of prostate cancer particularly in cases where collection of urine samples was performed after prostatic massage. Sarcosine and uracil in urine samples of patients with prostate cancer were not found as significant biomarkers for the diagnosis of prostate cancer. None of the three metabolites can be used reliably for monitoring the progress of the disease. Full article
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Open AccessArticle
Application of Passive Sampling to Characterise the Fish Exometabolome
Metabolites 2017, 7(1), 8; doi:10.3390/metabo7010008 -
Abstract
The endogenous metabolites excreted by organisms into their surrounding environment, termed the exometabolome, are important for many processes including chemical communication. In fish biology, such metabolites are also known to be informative markers of physiological status. While metabolomics is increasingly used to investigate
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The endogenous metabolites excreted by organisms into their surrounding environment, termed the exometabolome, are important for many processes including chemical communication. In fish biology, such metabolites are also known to be informative markers of physiological status. While metabolomics is increasingly used to investigate the endogenous biochemistry of organisms, no non-targeted studies of the metabolic complexity of fish exometabolomes have been reported to date. In environmental chemistry, Chemcatcher® (Portsmouth, UK) passive samplers have been developed to sample for micro-pollutants in water. Given the importance of the fish exometabolome, we sought to evaluate the capability of Chemcatcher® samplers to capture a broad spectrum of endogenous metabolites excreted by fish and to measure these using non-targeted direct infusion mass spectrometry metabolomics. The capabilities of C18 and styrene divinylbenzene reversed-phase sulfonated (SDB-RPS) Empore™ disks for capturing non-polar and polar metabolites, respectively, were compared. Furthermore, we investigated real, complex metabolite mixtures excreted from two model fish species, rainbow trout (Oncorhynchus mykiss) and three-spined stickleback (Gasterosteus aculeatus). In total, 344 biological samples and 28 QC samples were analysed, revealing 646 and 215 m/z peaks from trout and stickleback, respectively. The measured exometabolomes were principally affected by the type of Empore™ (Hemel Hempstead, UK) disk and also by the sampling time. Many peaks were putatively annotated, including several bile acids (e.g., chenodeoxycholate, taurocholate, glycocholate, glycolithocholate, glycochenodeoxycholate, glycodeoxycholate). Collectively these observations show the ability of Chemcatcher® passive samplers to capture endogenous metabolites excreted from fish. Full article
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Open AccessArticle
QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression
Metabolites 2017, 7(1), 7; doi:10.3390/metabo7010007 -
Abstract
Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed
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Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed by a reversed-phase chromatographic system under isocratic as well as pH and/or simultaneous pH and organic content gradient conditions. According to the proposed modification, an additional descriptor is added to a conventional QSRR expression, which is the analyte retention time, tR(R), measured under the same elution conditions, but in a second chromatographic column considered as a reference one. The 94 metabolites were studied on an Amide column using a Bare Silica column as a reference. For the second dataset, a Kinetex EVO C18 and a Gemini-NX column were used, where each of them was served as a reference column of the other. We found in all cases a significant improvement of the performance of the QSRR models when the descriptor tR(R) was considered. Full article
Open AccessArticle
Urine and Serum Metabolomics Analyses May Distinguish between Stages of Renal Cell Carcinoma
Metabolites 2017, 7(1), 6; doi:10.3390/metabo7010006 -
Abstract
Renal cell carcinoma (RCC) is a heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes. Preoperative fasting urine and serum samples were
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Renal cell carcinoma (RCC) is a heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes. Preoperative fasting urine and serum samples were collected from patients with clinical renal masses and assessed with 1H NMR and GCMS (gas chromatography-mass spectrometry) based metabolomics and multivariate statistical analysis. Alterations in levels of glycolytic and tricarboxylic acid (TCA) cycle intermediates were detected in RCC relative to benign masses. Orthogonal Partial Least Square Discriminant Analysis plots discriminated between benign vs. pT1 (R2 = 0.46, Q2 = 0.28; AUC = 0.83), benign vs. pT3 (R2 = 0.58, Q2 = 0.37; AUC = 0.87) for 1H NMR-analyzed serum and between benign vs. pT1 (R2 = 0.50, Q2 = 0.37; AUC = 0.83), benign vs. pT3 (R2 = 0.72, Q2 = 0.68, AUC = 0.98) for urine samples. Separation was observed between benign vs. pT3 (R2 = 0.63, Q2 = 0.48; AUC = 0.93), pT1 vs. pT3 (R2 = 0.70, Q2 = 0.54) for GCMS-analyzed serum and between benign vs. pT3 (R2Y = 0.87; Q2 = 0.70; AUC = 0.98) for urine samples. This pilot study suggests that urine and serum metabolomics may be useful in differentiating benign renal tumors from RCC and for staging RCC. Full article
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Open AccessArticle
NMR-Based Metabolic Profiling of Field-Grown Leaves from Sugar Beet Plants Harbouring Different Levels of Resistance to Cercospora Leaf Spot Disease
Metabolites 2017, 7(1), 4; doi:10.3390/metabo7010004 -
Abstract
Cercospora leaf spot (CLS) is one of the most serious leaf diseases for sugar beet (Beta vulgaris L.) worldwide. The breeding of sugar beet cultivars with both high CLS resistance and high yield is a major challenge for breeders. In this study,
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Cercospora leaf spot (CLS) is one of the most serious leaf diseases for sugar beet (Beta vulgaris L.) worldwide. The breeding of sugar beet cultivars with both high CLS resistance and high yield is a major challenge for breeders. In this study, we report the nuclear magnetic resonance (NMR)-based metabolic profiling of field-grown leaves for a subset of sugar beet genotypes harbouring different levels of CLS resistance. Leaves were collected from 12 sugar beet genotypes at four time points: seedling, early growth, root enlargement, and disease development stages. 1H-NMR spectra of foliar metabolites soluble in a deuterium-oxide (D2O)-based buffer were acquired and subjected to multivariate analyses. A principal component analysis (PCA) of the NMR data from the sugar beet leaves shows clear differences among the growth stages. At the later time points, the sugar and glycine betaine contents were increased, whereas the choline content was decreased. The relationship between the foliar metabolite profiles and resistance level to CLS was examined by combining partial least squares projection to latent structure (PLS) or orthogonal PLS (OPLS) analysis and univariate analyses. It was difficult to build a robust model for predicting precisely the disease severity indices (DSIs) of each genotype; however, GABA and Gln differentiated susceptible genotypes (genotypes with weak resistance) from resistant genotypes (genotypes with resistance greater than a moderate level) before inoculation tests. The results suggested that breeders might exclude susceptible genotypes from breeding programs based on foliar metabolites profiled without inoculation tests, which require an enormous amount of time and effort. Full article
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Open AccessArticle
Effects of Different Exercise Modes on the Urinary Metabolic Fingerprint of Men with and without Metabolic Syndrome
Metabolites 2017, 7(1), 5; doi:10.3390/metabo7010005 -
Abstract
Exercise is important in the prevention and treatment of the metabolic syndrome (MetS), a cluster of risk factors that raises morbidity. Metabolomics can facilitate the optimization of exercise prescription. This study aimed to investigate whether the response of the human urinary metabolic fingerprint
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Exercise is important in the prevention and treatment of the metabolic syndrome (MetS), a cluster of risk factors that raises morbidity. Metabolomics can facilitate the optimization of exercise prescription. This study aimed to investigate whether the response of the human urinary metabolic fingerprint to exercise depends on the presence of MetS or exercise mode. Twenty-three sedentary men (MetS, n = 9, and Healthy, n = 14) completed four trials: resting, high-intensity interval exercise (HIIE), continuous moderate-intensity exercise (CME), and resistance exercise (RE). Urine samples were collected pre-exercise and at 2, 4, and 24 h for targeted analysis by liquid chromatography-mass spectrometry. Time exerted the strongest differentiating effect, followed by exercise mode and health status. The greatest changes were observed in the first post-exercise samples, with a gradual return to baseline at 24 h. RE caused the greatest responses overall, followed by HIIE, while CME had minimal effect. The metabolic fingerprints of the two groups were separated at 2 h, after HIIE and RE; and at 4 h, after HIIE, with evidence of blunted response to exercise in MetS. Our findings show diverse responses of the urinary metabolic fingerprint to different exercise modes in men with and without metabolic syndrome. Full article
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Open AccessArticle
Distinguishing Benign from Malignant Pancreatic and Periampullary Lesions Using Combined Use of 1H-NMR Spectroscopy and Gas Chromatography–Mass Spectrometry
Metabolites 2017, 7(1), 3; doi:10.3390/metabo7010003 -
Abstract
Previous work demonstrated that serum metabolomics can distinguish pancreatic cancer from benign disease. However, in the clinic, non-pancreatic periampullary cancers are difficult to distinguish from pancreatic cancer. Therefore, to test the clinical utility of this technology, we determined whether any pancreatic and periampullary
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Previous work demonstrated that serum metabolomics can distinguish pancreatic cancer from benign disease. However, in the clinic, non-pancreatic periampullary cancers are difficult to distinguish from pancreatic cancer. Therefore, to test the clinical utility of this technology, we determined whether any pancreatic and periampullary adenocarcinoma could be distinguished from benign masses and biliary strictures. Sera from 157 patients with malignant and benign pancreatic and periampullary lesions were analyzed using proton nuclear magnetic resonance (1H-NMR) spectroscopy and gas chromatography–mass spectrometry (GC-MS). Multivariate projection modeling using SIMCA-P+ software in training datasets (n = 80) was used to generate the best models to differentiate disease states. Models were validated in test datasets (n = 77). The final 1H-NMR spectroscopy and GC-MS metabolomic profiles consisted of 14 and 18 compounds, with AUROC values of 0.74 (SE 0.06) and 0.62 (SE 0.08), respectively. The combination of 1H-NMR spectroscopy and GC-MS metabolites did not substantially improve this performance (AUROC 0.66, SE 0.08). In patients with adenocarcinoma, glutamate levels were consistently higher, while glutamine and alanine levels were consistently lower. Pancreatic and periampullary adenocarcinomas can be distinguished from benign lesions. To further enhance the discriminatory power of metabolomics in this setting, it will be important to identify the metabolomic changes that characterize each of the subclasses of this heterogeneous group of cancers. Full article
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Open AccessEditorial
Acknowledgement to Reviewers of Metabolites in 2016
Metabolites 2017, 7(1), 2; doi:10.3390/metabo7010002 -
Abstract The editors of Metabolites would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016. [...] Full article
Open AccessCommunication
Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS
Metabolites 2017, 7(1), 1; doi:10.3390/metabo7010001 -
Abstract
Gas Chromatography-Mass Spectrometry (GC-MS) has long been used for metabolite profiling of a wide range of biological samples. Many derivatisation protocols are already available and among these, trimethylsilyl (TMS) derivatisation is one of the most widely used in metabolomics. However, most TMS methods
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Gas Chromatography-Mass Spectrometry (GC-MS) has long been used for metabolite profiling of a wide range of biological samples. Many derivatisation protocols are already available and among these, trimethylsilyl (TMS) derivatisation is one of the most widely used in metabolomics. However, most TMS methods rely on off-line derivatisation prior to GC-MS analysis. In the case of manual off-line TMS derivatisation, the derivative created is unstable, so reduction in recoveries occurs over time. Thus, derivatisation is carried out in small batches. Here, we present a fully automated TMS derivatisation protocol using robotic autosamplers and we also evaluate a commercial software, Maestro available from Gerstel GmbH. Because of automation, there was no waiting time of derivatised samples on the autosamplers, thus reducing degradation of unstable metabolites. Moreover, this method allowed us to overlap samples and improved throughputs. We compared data obtained from both manual and automated TMS methods performed on three different matrices, including standard mix, wine, and plasma samples. The automated TMS method showed better reproducibility and higher peak intensity for most of the identified metabolites than the manual derivatisation method. We also validated the automated method using 114 quality control plasma samples. Additionally, we showed that this online method was highly reproducible for most of the metabolites detected and identified (RSD < 20) and specifically achieved excellent results for sugars, sugar alcohols, and some organic acids. To the very best of our knowledge, this is the first time that the automated TMS method has been applied to analyse a large number of complex plasma samples. Furthermore, we found that this method was highly applicable for routine metabolite profiling (both targeted and untargeted) in any metabolomics laboratory. Full article
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Open AccessArticle
Metabolomics with Nuclear Magnetic Resonance Spectroscopy in a Drosophila melanogaster Model of Surviving Sepsis
Metabolites 2016, 6(4), 47; doi:10.3390/metabo6040047 -
Abstract
Patients surviving sepsis demonstrate sustained inflammation, which has been associated with long-term complications. One of the main mechanisms behind sustained inflammation is a metabolic switch in parenchymal and immune cells, thus understanding metabolic alterations after sepsis may provide important insights to the pathophysiology
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Patients surviving sepsis demonstrate sustained inflammation, which has been associated with long-term complications. One of the main mechanisms behind sustained inflammation is a metabolic switch in parenchymal and immune cells, thus understanding metabolic alterations after sepsis may provide important insights to the pathophysiology of sepsis recovery. In this study, we explored metabolomics in a novel Drosophila melanogaster model of surviving sepsis using Nuclear Magnetic Resonance (NMR), to determine metabolite profiles. We used a model of percutaneous infection in Drosophila melanogaster to mimic sepsis. We had three experimental groups: sepsis survivors (infected with Staphylococcus aureus and treated with oral linezolid), sham (pricked with an aseptic needle), and unmanipulated (positive control). We performed metabolic measurements seven days after sepsis. We then implemented metabolites detected in NMR spectra into the MetExplore web server in order to identify the metabolic pathway alterations in sepsis surviving Drosophila. Our NMR metabolomic approach in a Drosophila model of recovery from sepsis clearly distinguished between all three groups and showed two different metabolomic signatures of inflammation. Sham flies had decreased levels of maltose, alanine, and glutamine, while their level of choline was increased. Sepsis survivors had a metabolic signature characterized by decreased glucose, maltose, tyrosine, beta-alanine, acetate, glutamine, and succinate. Full article
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Open AccessReview
Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems
Metabolites 2016, 6(4), 46; doi:10.3390/metabo6040046 -
Abstract
Although significant advances have been made in recent years, the structural elucidation of small molecules continues to remain a challenging issue for metabolite profiling. Many metabolomic studies feature unknown compounds; sometimes even in the list of features identified as “statistically significant” in the
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Although significant advances have been made in recent years, the structural elucidation of small molecules continues to remain a challenging issue for metabolite profiling. Many metabolomic studies feature unknown compounds; sometimes even in the list of features identified as “statistically significant” in the study. Such metabolic “dark matter” means that much of the potential information collected by metabolomics studies is lost. Accurate structure elucidation allows researchers to identify these compounds. This in turn, facilitates downstream metabolite pathway analysis, and a better understanding of the underlying biology of the system under investigation. This review covers a range of methods for the structural elucidation of individual compounds, including those based on gas and liquid chromatography hyphenated to mass spectrometry, single and multi-dimensional nuclear magnetic resonance spectroscopy, and high-resolution mass spectrometry and includes discussion of data standardization. Future perspectives in structure elucidation are also discussed; with a focus on the potential development of instruments and techniques, in both nuclear magnetic resonance spectroscopy and mass spectrometry that, may help solve some of the current issues that are hampering the complete identification of metabolite structure and function. Full article
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Open AccessReview
Quantification of Microbial Phenotypes
Metabolites 2016, 6(4), 45; doi:10.3390/metabo6040045 -
Abstract
Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current
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Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis. Full article
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Open AccessReview
The Metabolic Implications of Glucocorticoids in a High-Fat Diet Setting and the Counter-Effects of Exercise
Metabolites 2016, 6(4), 44; doi:10.3390/metabo6040044 -
Abstract
Glucocorticoids (GCs) are steroid hormones, naturally produced by activation of the hypothalamic-pituitary-adrenal (HPA) axis, that mediate the immune and metabolic systems. Synthetic GCs are used to treat a number of inflammatory conditions and diseases including lupus and rheumatoid arthritis. Generally, chronic or high
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Glucocorticoids (GCs) are steroid hormones, naturally produced by activation of the hypothalamic-pituitary-adrenal (HPA) axis, that mediate the immune and metabolic systems. Synthetic GCs are used to treat a number of inflammatory conditions and diseases including lupus and rheumatoid arthritis. Generally, chronic or high dose GC administration is associated with side effects such as steroid-induced skeletal muscle loss, visceral adiposity, and diabetes development. Patients who are taking exogenous GCs could also be more susceptible to poor food choices, but the effect that increasing fat consumption in combination with elevated exogenous GCs has only recently been investigated. Overall, these studies show that the damaging metabolic effects initiated through exogenous GC treatment are significantly amplified when combined with a high fat diet (HFD). Rodent studies of a HFD and elevated GCs demonstrate more glucose intolerance, hyperinsulinemia, visceral adiposity, and skeletal muscle lipid deposition when compared to rodents subjected to either treatment on its own. Exercise has recently been shown to be a viable therapeutic option for GC-treated, high-fat fed rodents, with the potential mechanisms still being examined. Clinically, these mechanistic studies underscore the importance of a low fat diet and increased physical activity levels when individuals are given a course of GC treatment. Full article
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Open AccessArticle
Detection of Volatile Metabolites Derived from Garlic (Allium sativum) in Human Urine
Metabolites 2016, 6(4), 43; doi:10.3390/metabo6040043 -
Abstract
The metabolism and excretion of flavor constituents of garlic, a common plant used in flavoring foods and attributed with several health benefits, in humans is not fully understood. Likewise, the physiologically active principles of garlic have not been fully clarified to date. It
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The metabolism and excretion of flavor constituents of garlic, a common plant used in flavoring foods and attributed with several health benefits, in humans is not fully understood. Likewise, the physiologically active principles of garlic have not been fully clarified to date. It is possible that not only the parent compounds present in garlic but also its metabolites are responsible for the specific physiological properties of garlic, including its influence on the characteristic body odor signature of humans after garlic consumption. Accordingly, the aim of this study was to investigate potential garlic-derived metabolites in human urine. To this aim, 14 sets of urine samples were obtained from 12 volunteers, whereby each set comprised one sample that was collected prior to consumption of food-relevant concentrations of garlic, followed by five to eight subsequent samples after garlic consumption that covered a time interval of up to 26 h. The samples were analyzed chemo-analytically using gas chromatography-mass spectrometry/olfactometry (GC-MS/O), as well as sensorially by a trained human panel. The analyses revealed three different garlic-derived metabolites in urine, namely allyl methyl sulfide (AMS), allyl methyl sulfoxide (AMSO) and allyl methyl sulfone (AMSO2), confirming our previous findings on human milk metabolite composition. The excretion rates of these metabolites into urine were strongly time-dependent with distinct inter-individual differences. These findings indicate that the volatile odorant fraction of garlic is heavily biotransformed in humans, opening up a window into substance circulation within the human body with potential wider ramifications in view of physiological effects of this aromatic plant that is appreciated by humans in their daily diet. Full article
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Open AccessArticle
A Simple Method for Measuring Carbon-13 Fatty Acid Enrichment in the Major Lipid Classes of Microalgae Using GC-MS
Metabolites 2016, 6(4), 42; doi:10.3390/metabo6040042 -
Abstract
A simple method for tracing carbon fixation and lipid synthesis in microalgae was developed using a combination of solid-phase extraction (SPE) and negative ion chemical ionisation gas chromatography mass spectrometry (NCI-GC-MS). NCI-GC-MS is an extremely sensitive technique that can produce an unfragmented molecular
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A simple method for tracing carbon fixation and lipid synthesis in microalgae was developed using a combination of solid-phase extraction (SPE) and negative ion chemical ionisation gas chromatography mass spectrometry (NCI-GC-MS). NCI-GC-MS is an extremely sensitive technique that can produce an unfragmented molecular ion making this technique particularly useful for stable isotope enrichment studies. Derivatisation of fatty acids using pentafluorobenzyl bromide (PFBBr) allows the coupling of the high separation efficiency of GC and the measurement of unfragmented molecular ions for each of the fatty acids by single quadrupole MS. The key is that isotope spectra can be measured without interference from co-eluting fatty acids or other molecules. Pre-fractionation of lipid extracts by SPE allows the measurement of 13C isotope incorporation into the three main lipid classes (phospholipids, glycolipids, neutral lipids) in microalgae thus allowing the study of complex lipid biochemistry using relatively straightforward analytical technology. The high selectivity of GC is necessary as it allows the collection of mass spectra for individual fatty acids, including cis/trans isomers, of the PFB-derivatised fatty acids. The combination of solid-phase extraction and GC-MS enables the accurate determination of 13C incorporation into each lipid pool. Three solvent extraction protocols that are commonly used in lipidomics were also evaluated and are described here with regard to extraction efficiencies for lipid analysis in microalgae. Full article
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Open AccessFeature PaperArticle
Staphylococcus aureus Infection Reduces Nutrition Uptake and Nucleotide Biosynthesis in a Human Airway Epithelial Cell Line
Metabolites 2016, 6(4), 41; doi:10.3390/metabo6040041 -
Abstract
The Gram positive opportunistic human pathogen Staphylococcus aureus induces a variety of diseases including pneumonia. S. aureus is the second most isolated pathogen in cystic fibrosis patients and accounts for a large proportion of nosocomial pneumonia. Inside the lung, the human airway epithelium
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The Gram positive opportunistic human pathogen Staphylococcus aureus induces a variety of diseases including pneumonia. S. aureus is the second most isolated pathogen in cystic fibrosis patients and accounts for a large proportion of nosocomial pneumonia. Inside the lung, the human airway epithelium is the first line in defence with regard to microbial recognition and clearance as well as regulation of the immune response. The metabolic host response is, however, yet unknown. To address the question of whether the infection alters the metabolome and metabolic activity of airway epithelial cells, we used a metabolomics approach. The nutrition uptake by the human airway epithelial cell line A549 was monitored over time by proton magnetic resonance spectroscopy (1H-NMR) and the intracellular metabolic fingerprints were investigated by gas chromatography and high performance liquid chromatography (GC-MS) and (HPLC-MS). To test the metabolic activity of the host cells, glutamine analogues and labelled precursors were applied after the infection. We found that A549 cells restrict uptake of essential nutrients from the medium after S. aureus infection. Moreover, the infection led to a shutdown of the purine and pyrimidine synthesis in the A549 host cell, whereas other metabolic routes such as the hexosamine biosynthesis pathway remained active. In summary, our data show that the infection with S. aureus negatively affects growth, alters the metabolic composition and specifically impacts the de novo nucleotide biosynthesis in this human airway epithelial cell model. Full article
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Open AccessArticle
A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps
Metabolites 2016, 6(4), 40; doi:10.3390/metabo6040040 -
Abstract
Untargeted metabolomic studies generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit. Despite the remarkable progress in the development of tools and algorithms, the “exhaustive” extraction of information from these metabolomic datasets is still a non-trivial undertaking. A
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Untargeted metabolomic studies generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit. Despite the remarkable progress in the development of tools and algorithms, the “exhaustive” extraction of information from these metabolomic datasets is still a non-trivial undertaking. A conversation on data mining strategies for a maximal information extraction from metabolomic data is needed. Using a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomic dataset, this study explored the influence of collection parameters in the data pre-processing step, scaling and data transformation on the statistical models generated, and feature selection, thereafter. Data obtained in positive mode generated from a LC-MS-based untargeted metabolomic study (sorghum plants responding dynamically to infection by a fungal pathogen) were used. Raw data were pre-processed with MarkerLynxTM software (Waters Corporation, Manchester, UK). Here, two parameters were varied: the intensity threshold (50–100 counts) and the mass tolerance (0.005–0.01 Da). After the pre-processing, the datasets were imported into SIMCA (Umetrics, Umea, Sweden) for more data cleaning and statistical modeling. In addition, different scaling (unit variance, Pareto, etc.) and data transformation (log and power) methods were explored. The results showed that the pre-processing parameters (or algorithms) influence the output dataset with regard to the number of defined features. Furthermore, the study demonstrates that the pre-treatment of data prior to statistical modeling affects the subspace approximation outcome: e.g., the amount of variation in X-data that the model can explain and predict. The pre-processing and pre-treatment steps subsequently influence the number of statistically significant extracted/selected features (variables). Thus, as informed by the results, to maximize the value of untargeted metabolomic data, understanding of the data structures and exploration of different algorithms and methods (at different steps of the data analysis pipeline) might be the best trade-off, currently, and possibly an epistemological imperative. Full article
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Open AccessArticle
MetMatch: A Semi-Automated Software Tool for the Comparison and Alignment of LC-HRMS Data from Different Metabolomics Experiments
Metabolites 2016, 6(4), 39; doi:10.3390/metabo6040039 -
Abstract
Due to its unsurpassed sensitivity and selectivity, LC-HRMS is one of the major analytical techniques in metabolomics research. However, limited stability of experimental and instrument parameters may cause shifts and drifts of retention time and mass accuracy or the formation of different ion
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Due to its unsurpassed sensitivity and selectivity, LC-HRMS is one of the major analytical techniques in metabolomics research. However, limited stability of experimental and instrument parameters may cause shifts and drifts of retention time and mass accuracy or the formation of different ion species, thus complicating conclusive interpretation of the raw data, especially when generated in different analytical batches. Here, a novel software tool for the semi-automated alignment of different measurement sequences is presented. The tool is implemented in the Java programming language, it features an intuitive user interface and its main goal is to facilitate the comparison of data obtained from different metabolomics experiments. Based on a feature list (i.e., processed LC-HRMS chromatograms with mass-to-charge ratio (m/z) values and retention times) that serves as a reference, the tool recognizes both m/z and retention time shifts of single or multiple analytical datafiles/batches of interest. MetMatch is also designed to account for differently formed ion species of detected metabolites. Corresponding ions and metabolites are matched and chromatographic peak areas, m/z values and retention times are combined into a single data matrix. The convenient user interface allows for easy manipulation of processing results and graphical illustration of the raw data as well as the automatically matched ions and metabolites. The software tool is exemplified with LC-HRMS data from untargeted metabolomics experiments investigating phenylalanine-derived metabolites in wheat and T-2 toxin/HT-2 toxin detoxification products in barley. Full article
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Open AccessArticle
Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding
Metabolites 2016, 6(4), 38; doi:10.3390/metabo6040038 -
Abstract
Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate
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Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate multiple, potentially interacting, factors simultaneously following a specific experimental design. Such data often cannot be considered as a “pure” regression or a classification problem. Nevertheless, these data have often still been treated as a regression or classification problem and this could lead to ambiguous results. In this study, we investigated the feasibility of designing a hybrid target matrix Y that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of Y coding was based on the same principle used by structural modelling in machine learning techniques. Two real metabolomics datasets were used as examples to illustrate how the new Y coding can improve the interpretability of the PLS model compared to classic regression/classification coding. Full article
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