Next Issue
Previous Issue

Table of Contents

Metabolites, Volume 7, Issue 1 (March 2017)

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Cover Story Exercise plays a primary role in the treatment of the metabolic syndrome (MetS), a cluster of risk [...] Read more.
View options order results:
result details:
Displaying articles 1-11
Export citation of selected articles as:

Editorial

Jump to: Research

Open AccessEditorial Acknowledgement to Reviewers of Metabolites in 2016
Metabolites 2017, 7(1), 2; doi:10.3390/metabo7010002
Received: 10 January 2017 / Revised: 10 January 2017 / Accepted: 10 January 2017 / Published: 11 January 2017
PDF Full-text (160 KB) | HTML Full-text | XML Full-text
Abstract
The editors of Metabolites would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016. [...] Full article

Research

Jump to: Editorial

Open AccessCommunication Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS
Metabolites 2017, 7(1), 1; doi:10.3390/metabo7010001
Received: 23 September 2016 / Revised: 22 December 2016 / Accepted: 26 December 2016 / Published: 29 December 2016
Cited by 1 | PDF Full-text (14284 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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
Figures

Figure 1

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
Received: 13 April 2016 / Revised: 9 December 2016 / Accepted: 8 January 2017 / Published: 13 January 2017
Cited by 1 | PDF Full-text (2062 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
[...] Read more.
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
(This article belongs to the Special Issue Cancer Metabolomics 2016)
Figures

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
Received: 31 August 2016 / Revised: 17 January 2017 / Accepted: 23 January 2017 / Published: 26 January 2017
Cited by 3 | PDF Full-text (2235 KB) | HTML Full-text | XML Full-text | Supplementary Files
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,
[...] Read more.
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
(This article belongs to the Special Issue Challenging Biochemical Complexities by NMR)
Figures

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
Received: 27 December 2016 / Revised: 20 January 2017 / Accepted: 24 January 2017 / Published: 26 January 2017
Cited by 4 | PDF Full-text (3018 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
[...] Read more.
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
(This article belongs to the Special Issue Metabolomics 2016)
Figures

Open AccessArticle Urine and Serum Metabolomics Analyses May Distinguish between Stages of Renal Cell Carcinoma
Metabolites 2017, 7(1), 6; doi:10.3390/metabo7010006
Received: 17 November 2016 / Revised: 18 January 2017 / Accepted: 26 January 2017 / Published: 3 February 2017
Cited by 1 | PDF Full-text (1698 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
[...] Read more.
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
Figures

Figure 1

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
Received: 21 December 2016 / Accepted: 5 February 2017 / Published: 9 February 2017
Cited by 1 | PDF Full-text (178 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
[...] Read more.
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
(This article belongs to the Special Issue Metabolomics 2016)
Open AccessArticle Application of Passive Sampling to Characterise the Fish Exometabolome
Metabolites 2017, 7(1), 8; doi:10.3390/metabo7010008
Received: 22 December 2016 / Revised: 30 January 2017 / Accepted: 10 February 2017 / Published: 14 February 2017
PDF Full-text (1367 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
[...] Read more.
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
(This article belongs to the Special Issue Environmental Metabolomics)
Figures

Figure 1

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
Received: 29 December 2016 / Revised: 14 February 2017 / Accepted: 18 February 2017 / Published: 23 February 2017
Cited by 1 | PDF Full-text (1711 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
[...] Read more.
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
(This article belongs to the Special Issue Metabolomics 2016)
Figures

Figure 1

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
Received: 2 January 2017 / Revised: 13 February 2017 / Accepted: 18 February 2017 / Published: 23 February 2017
Cited by 1 | PDF Full-text (2167 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
[...] Read more.
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
(This article belongs to the Special Issue Metabolomics 2016)
Figures

Figure 1

Open AccessArticle Defining the Baseline and Oxidant Perturbed Lipidomic Profiles of Daphnia magna
Metabolites 2017, 7(1), 11; doi:10.3390/metabo7010011
Received: 23 January 2017 / Revised: 3 March 2017 / Accepted: 11 March 2017 / Published: 15 March 2017
Cited by 1 | PDF Full-text (1575 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Recent technological advancement has enabled the emergence of lipidomics as an important tool for assessing molecular stress, one which has yet to be assessed fully as an approach in an environmental toxicological context. Here we have applied a high-resolution, non-targeted, nanoelectrospray ionisation (nESI)
[...] Read more.
Recent technological advancement has enabled the emergence of lipidomics as an important tool for assessing molecular stress, one which has yet to be assessed fully as an approach in an environmental toxicological context. Here we have applied a high-resolution, non-targeted, nanoelectrospray ionisation (nESI) direct infusion mass spectrometry (DIMS) technique to assess the effects of oxidative stress to Daphnia magna both in vitro (air exposure of daphniid extracts) and in vivo (Cu2+ exposure). Multivariate and univariate statistical analyses were used to distinguish any perturbations including oxidation to the D. magna baseline lipidome. This approach enabled the putative annotation of the baseline lipidome of D. magna with 65% of the lipid species discovered previously not reported. In vitro exposure of lipid extracts to air, primarily to test the methodology, revealed a significant perturbation to this baseline lipidome with detectable oxidation of peaks, in most cases attributed to single oxygen addition. Exposure of D. magna to Cu2+ in vivo also caused a significant perturbation to the lipidome at an environmentally relevant concentration of 20 µg/L. This nESI DIMS approach has successfully identified perturbations and oxidative modifications to the D. magna lipidome in a high-throughput manner, highlighting its suitability for environmental lipidomic studies. Full article
(This article belongs to the Special Issue Environmental Metabolomics)
Figures

Figure 1

Back to Top