Metabolic characterization of Palatinate German white wines according to sensory attributes, varieties, and vintages using NMR spectroscopy and multivariate data analyses

(1)H NMR (nuclear magnetic resonance spectroscopy) has been used for metabolomic analysis of 'Riesling' and 'Mueller-Thurgau' white wines from the German Palatinate region. Diverse two-dimensional NMR techniques have been applied for the identification of metabolites, including phenolics. It is shown that sensory analysis correlates with NMR-based metabolic profiles of wine. (1)H NMR data in combination with multivariate data analysis methods, like principal component analysis (PCA), partial least squares projections to latent structures (PLS), and bidirectional orthogonal projections to latent structures (O2PLS) analysis, were employed in an attempt to identify the metabolites responsible for the taste of wine, using a non-targeted approach. The high quality wines were characterized by elevated levels of compounds like proline, 2,3-butanediol, malate, quercetin, and catechin. Characterization of wine based on type and vintage was also done using orthogonal projections to latent structures (OPLS) analysis. 'Riesling' wines were characterized by higher levels of catechin, caftarate, valine, proline, malate, and citrate whereas compounds like quercetin, resveratrol, gallate, leucine, threonine, succinate, and lactate, were found discriminating for 'Mueller-Thurgau'. The wines from 2006 vintage were dominated by leucine, phenylalanine, citrate, malate, and phenolics, while valine, proline, alanine, and succinate were predominantly present in the 2007 vintage. Based on these results, it can be postulated the NMR-based metabolomics offers an easy and comprehensive analysis of wine and in combination with multivariate data analyses can be used to investigate the source of the wines and to predict certain sensory aspects of wine.     

A study of metabolic compartmentation in the rat heart and cardiac mitochondria using high-resolution magic angle spinning 1H NMR spectroscopy

High-resolution magic angle spinning (MAS) (1)H nuclear magnetic resonance (NMR) spectroscopy is increasingly being used to monitor metabolic abnormalities within cells and intact tissues. Many toxicological insults and metabolic diseases affect subcellular organelles, particularly mitochondria. In this study high-resolution (1)H NMR spectroscopy was used to examine metabolic compartmentation between the cytosol and mitochondria in the rat heart to investigate whether biomarkers of mitochondrial dysfunction could be identified and further define the mitochondrial environment. High-resolution MAS spectra of mitochondria revealed NMR signals from lactate, alanine, taurine, choline, phosphocholine, creatine, glycine and lipids. However, spectra from mitochondrial extracts contained additional well-resolved resonances from valine, methionine, glutamine, acetoacetate, succinate, and aspartate, suggesting that a number of metabolites bound within the mitochondrial membranes occur in 'NMR invisible' environments. This effect was further investigated using diffusion-weighted measurements of water and NMR spectroscopy during state 2 and state 3 respiration. State 3 respiration caused a decrease in the resonance intensity of endogenous succinate compared with state 2 respiration, suggesting that coupled respiration may also modulate the NMR detection of metabolites within mitochondria.     

Identification of bioactive metabolites against adenosine A1 receptor using NMR-based metabolomics

Marine sponges are relatively less explored for their chemical features but highly anticipated resource for bioactive compounds. In this paper we report the screening of marine sponges crude extracts for their potential to bind the adenosine A1 receptor. Many samples showed very promising activity and in order to identify the active components, a metabolomics-chemometrics approach is employed. Nuclear magnetic resonance spectroscopy is used for the metabolic profiling of the marine sponges and partial least squares (PLS) and orthogonal PLS (OPLS) algorithms are used to correlate the metabolomics with bioactivity data. Using several two dimensional-NMR techniques, the resonances responsible for the separation of high activity samples from the medium and low activity samples were identified as associated to metabolites like halisulfate 1, halisulfate 3–5, and suvanine (1–5), all belongs to sesterterpenes class. The reference compounds for these metabolites are also tested for the activity, which endorse the findings of the applied methodology.     

Biological 1H NMR spectroscopy

Proton nuclear magnetic resonance spectroscopy (1H NMR) is a powerful analytical method used to identify and quantitate chemical compounds. In recent years, it has been used to study rates of metabolism in microbes, isolated perfused tissues, intact animals, and human beings. This review highlights some of the more recent biological applications of 1H NMR in the study of metabolic pathophysiology in animals and man. 1H NMR can rapidly analyze complex mixtures of metabolites found in body fluid and biopsy specimens. In vivo 1H NMR methods can measure intracellular pH, a wide variety of metabolites, tissue perfusion, and rates of metabolism of endogenous and exogenous compounds. Using 13C labeled compounds or magnetization transfer techniques metabolic fluxes may be measured in vivo during virtually all normal and abnormal physiological conditions.     

Comparison of 1D and 2D NMR spectroscopy for metabolic profiling

High-resolution, liquid state nuclear magnetic resonance (NMR) spectroscopy is a popular platform for metabolic profiling because the technique is nondestructive, quantitative, reproducible, and the spectra contain a wealth of biochemical information. Because of the large dynamic range of metabolite concentrations in biofluids, statistical analyses of one-dimensional (1D) proton NMR data tend to be biased toward selecting changes in more abundant metabolites. Although two-dimensional (2D) proton-proton experiments can alleviate spectral crowding, they have been mainly used for structural determination. In this study, 2D total correlation spectroscopy NMR was used to compare the global metabolic profiles of urine obtained from wild-type and Abcc6-knockout mice. The 2D data were compared to an improved 1D experiment in which signal contributions from macromolecules and the urea peak have been spectroscopically removed for more accurate quantitation of low-abundance metabolites. Although statistical models from both 1D and 2D data could differentiate samples acquired from the two groups of mice, only the 2D spectra allowed the characterization of statistically relevant changes in the low-abundance metabolites. While acquisition of the 2D data require more time, the data obtained resulted in a more meaningful and comprehensive metabolic profile, aided in metabolite identifications, and minimized ambiguities in peak assignments.     

Quantitative metabolomic profiling of serum and urine in DSS-induced ulcerative colitis of mice by (1)H NMR spectroscopy

Quantitative profiling of a large number of metabolic compounds is a promising method to detect biomarkers in inflammatory bowel diseases (IBD), such as ulcerative colitis (UC). We induced an experimental form of UC in mice by treatment with dextran sulfate sodium (DSS) and characterized 53 serum and 69 urine metabolites by use of (1)H NMR spectroscopy and quantitative ("targeted") analysis to distinguish between diseased and healthy animals. Hierarchical multivariate orthogonal partial least-squares (OPLS) models were developed to detect and predict separation of control and DSS-treated mice. DSS treatment resulted in weight loss, colonic inflammation, and increase in myeloperoxidase activity. Metabolomic patterns generated from the OPLS data clearly separated DSS-treated from control mice with a slightly higher predictive power (Q(2)) for serum (0.73) than urine (0.71). During DSS colitis, creatine, carnitine, and methylamines increased in urine while in serum, maximal increases were observed for ketone bodies, hypoxanthine, and tryptophan. Antioxidant metabolites decreased in urine whereas in serum, glucose and Krebs cycle intermediates decreased strongly. Quantitative metabolic profiling of serum and urine thus discriminates between healthy and DSS-treated mice. Analysis of serum or urine seems to be equally powerful for detecting experimental colitis, and a combined analysis offers only a minor improvement.     

Use of the Local False Discovery Rate for Identification of Metabolic Biomarkers in Rat Urine Following Genkwa Flos-Induced Hepatotoxicity

Metabolomics is concerned with characterizing the large number of metabolites present in a biological system using nuclear magnetic resonance (NMR) and HPLC/MS (high-performance liquid chromatography with mass spectrometry). Multivariate analysis is one of the most important tools for metabolic biomarker identification in metabolomic studies. However, analyzing the large-scale data sets acquired during metabolic fingerprinting is a major challenge. As a posterior probability that the features of interest are not affected, the local false discovery rate (LFDR) is a good interpretable measure. However, it is rarely used to when interrogating metabolic data to identify biomarkers. In this study, we employed the LFDR method to analyze HPLC/MS data acquired from a metabolomic study of metabolic changes in rat urine during hepatotoxicity induced by Genkwa flos (GF) treatment. The LFDR approach was successfully used to identify important rat urine metabolites altered by GF-stimulated hepatotoxicity. Compared with principle component analysis (PCA), LFDR is an interpretable measure and discovers more important metabolites in an HPLC/MS-based metabolomic study.     

Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.     

NMR natural abundance estimation of13C-metabolic solutes in normal and habituated sugarbeet cell lines

Summary NMR (nuclear magnetic resonance) spectroscopy was used to identify metabolic solutes in one normal and two habituated sugarbeet cell lines (Beta vulgaris L.altissima) obtained from the same mother strain. This technique was applied to investigate the intracellular naturally occurring¹³C isotopes (1.1% of total natural carbon) in living sugarbeet suspension cells and perchloric cell extracts. A combination of¹H,¹³C, double-quantum filter correlation spectroscopy, heteronuclear multiple-bond correlation, and heteronuclear multiple-quantum coherence spectra from perchloric cell extracts enabled us to identify the main compounds in the different extract solutions. This was verified by spiking the solutions with small amounts of reference compounds to exclude the influence exerted by pH on the chemical shifts of the different compounds in the¹H and¹³C spectra. The comparison of the three sugarbeet cell lines' NMR spectra showed the presence of sucrose, glucose, and fructose in the three strains. On the other hand, it revealed a strong discrepancy between metabolic solutes. Spectra from the habituated lines showed the presence of glutamine. Some amino acids such as alanine or valine, and unidentified signals corresponding to aromatic rings were only characterized in the habituated nonorganogenic cells. On the basis of these¹³C NMR data we assumed that the discrepancy between the different sugarbeet cell lines could be due to an increase in the metabolic activity of the habituated cell lines in relation to their autonomous growth.Abbreviations DQF-COSY double-quantum filter correlation spectroscopy - HO habituated organogenous - HNO habituated nonorganogenous - HMBC heteronuclear multiple-bond correlation - HMQC heteronuclear multiple-quantum coherence - N normal - NMR nuclear magnetic resonance - TSP sodium tetradeutero-3-(trimethylsilyl)-propionate     

Discrimination of Basal Cell Carcinoma from Normal Skin Tissue Using High-Resolution Magic Angle Spinning 1H NMR Spectroscopy

High-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy is a useful tool for investigating the metabolism of various cancers. Basal cell carcinoma (BCC) is the most common skin cancer. However, to our knowledge, data on metabolic profiling of BCC have not been reported in the literature. The objective of the present study was to investigate the metabolic profiling of cutaneous BCC using HR-MAS ¹H NMR spectroscopy. HR-MAS ¹H NMR spectroscopy was used to analyze the metabolite profile and metabolite intensity of histopathologically confirmed BCC tissues and normal skin tissue (NST) samples. The metabolic intensity normalized to the total spectral intensities in BCC and NST was compared, and multivariate analysis was performed with orthogonal partial least-squares discriminant analysis (OPLS-DA). P values < 0.05 were considered statistically significant. Univariate analysis revealed 9 metabolites that showed statistically significant difference between BCC and NST. In multivariate analysis, the OPLS-DA models built with the HR-MAS NMR metabolic profiles revealed a clear separation of BCC from NST. The receiver operating characteristic curve generated from the results revealed an excellent discrimination of BCC from NST with an area under the curve (AUC) value of 0.961. The present study demonstrated that the metabolite profile and metabolite intensity differ between BCC and NST, and that HR-MAS ¹H NMR spectroscopy can be a valuable tool in the diagnosis of BCC.     

Observation of Cerebral Metabolites in an Animal Model of Acute Liver Failure In Vivo: A 1H and 31P Nuclear Magnetic Resonance Study

Acute liver failure was induced in rats by a single intragastric dose of carbon tetrachloride. This causes hepatic centrilobular necrosis, as indicated by histological examinations, and produces a large increase in the activity of serum alanine aminotransferase. The plasma NH4+ level (mean +/- SEM) was 123 +/- 10 microM in the control group and 564 +/- 41 microM in animals with acute liver failure (each n = 5). 31P nuclear magnetic resonance (NMR) was used to monitor brain cortical high-energy phosphate compounds, Pi, and intracellular pH. 1H NMR spectroscopy was utilised to detect additional metabolites, including glutamate, glutamine, and lactate. The results show that the forebrain is capable of maintaining normal phosphorus energy metabolite ratios and intracellular pH despite the metabolic challenge by an elevated blood NH4+ level. There was a significant increase in the brain glutamine level and a concomitant decrease in the glutamate level during hyperammonaemia. The brain lactate level increased twofold in rats with acute liver failure. The results indicate that 1H NMR can be used to detect cerebral metabolic changes in this model of hyperammonaemia, and our observations are discussed in relation to compartmentation of NH4+ metabolism.     

Metabonomics evaluation of urine from rats given acute and chronic doses of acetaminophen using NMR and UPLC/MS

Urinary metabolic perturbations associated with acute and chronic acetaminophen-induced hepatotoxicity were investigated using nuclear magnetic resonance (NMR) spectroscopy and ultra performance liquid chromatography/mass spectrometry (UPLC/MS) metabonomics approaches to determine biomarkers of hepatotoxicity. Acute and chronic doses of acetaminophen (APAP) were administered to male Sprague-Dawley rats. NMR and UPLC/MS were able to detect both drug metabolites and endogenous metabolites simultaneously. The principal component analysis (PCA) of NMR or UPLC/MS spectra showed that metabolic changes observed in both acute and chronic dosing of acetaminophen were similar. Histopathology and clinical chemistry studies were performed and correlated well with the PCA analysis and magnitude of metabolite changes. Depletion of antioxidants (e.g. ferulic acid), trigonelline, S-adenosyl-l-methionine, and energy-related metabolites indicated that oxidative stress was caused by acute and chronic acetaminophen administration. Similar patterns of metabolic changes in response to acute or chronic dosing suggest similar detoxification and recovery mechanisms following APAP administration.     

Metabolomics of the Tumor Microenvironment in Pediatric Acute Lymphoblastic Leukemia

The tumor microenvironment is emerging as an important therapeutic target. Most studies, however, are focused on the protein components, and relatively little is known of how the microenvironmental metabolome might influence tumor survival. In this study, we examined the metabolic profiles of paired bone marrow (BM) and peripheral blood (PB) samples from 10 children with acute lymphoblastic leukemia (ALL). BM and PB samples from the same patient were collected at the time of diagnosis and after 29 days of induction therapy, at which point all patients were in remission. We employed two analytical platforms, high-resolution magnetic resonance spectroscopy and gas chromatography-mass spectrometry, to identify and quantify 102 metabolites in the BM and PB. Standard ALL therapy, which includes l-asparaginase, completely removed circulating asparagine, but not glutamine. Statistical analyses of metabolite correlations and network reconstructions showed that the untreated BM microenvironment was characterized by a significant network-level signature: a cluster of highly correlated lipids and metabolites involved in lipid metabolism (p<0.006). In contrast, the strongest correlations in the BM upon remission were observed among amino acid metabolites and derivatives (p<9.2×10^-10). This study provides evidence that metabolic characterization of the cancer niche could generate new hypotheses for the development of cancer therapies.     

Distinguishing transgenic from non-transgenic Arabidopsis plants by 1H NMR-based metabolic fingerprinting

We have recently reported the construction of an nuclear magnetic resonance （NMR）-based metabonomics study platform, Automics. To examine the application of Automics in transgenic plants, we performed metabolic fingerprinting analysis, i.e., 1H NMR spectroscopy and multivariate analysis, on wild-type and transgenic Arabidopsis. We found that it was possible to distinguish wild-type from four transgenic plants by PLS-DA following application of orthogonal signal correction （OSC）. Scores plot following OSC clearly demonstrates significant variation between the transgenic and non-transgenic groups, suggesting that the metabolic changes among wild-type and transgenic lines are possibly associated with transgenic event, We also found that the major contributing metabolites were some specific amino acids （i.e., threonine and alanine）, which could correspond to the insertion of the selective marker BAR gene in the transgenic plants. Our data suggests that NMR-based metabonomics is an efficient method to distinguish fingerprinting difference between wild-type and transgenic plants, and can potentially be applied in the bio-safety assessment of transgenic plants.     

Distinguishing transgenic from non-transgenic Arabidopsis plants by 1H NMR-based metabolic fingerprinting

We have recently reported the construction of an nuclear magnetic resonance (NMR)-based metabonomics study platform, Automics.To examine the application of Automics in transgenic plants, we performed metabolic fingerprinting analysis, i.e., 1H NMR spectroscopy and multivariate analysis, on wild-type and transgenic Arabidopsis. We found that it was possible to distinguish wild-type from four transgenic plants by PLS-DA following application of orthogonal signal correction (OSC). Scores plot following OSC clearly demonstrates significant variation between the transgenic and non-transgenic groups, suggesting that the metabolic changes among wild-type and transgenic lines are possibly associated with transgenic event. We also found that the major contributing metabolites were some specific amino acids (i.e., threonine and alanine), which could correspond to the insertion of the selective marker BAR gene in the transgenic plants. Our data suggests that NMR-based metabonomics is an efficient method to distinguish fingerprinting difference between wild-type and transgenic plants, and can potentially be applied in the bio-safety assessment of transgenic plants.     

1H-NMR-based metabolomics for cancer targeting and metabolic engineering –A review

Nuclear magnetic resonance (NMR) spectroscopy acts as the best tool that can be used in tissue engineering scaffolds to investigate unknown metabolites. Moreover, metabolomics is a systems approach for examining in vivo and in vitro metabolic profiles, which promises to provide data on cancer metabolic alterations. However, metabolomic profiling allows for the activity of small molecules and metabolic alterations to be measured. Furthermore, metabolic profiling also provides high-spectral resolution, which can then be linked to potential metabolic relationships. An altered metabolism is a hallmark of cancer that can control many malignant properties to drive tumorigenesis. Metabolite targeting and metabolic engineering contribute to carcinogenesis by proliferation, and metabolic differentiation. The resulting the metabolic differences are examined with traditional chemometric methods such as principal component analysis (PCA), and partial least squares-discriminate analysis (PLS-DA). In this review, we examine NMR-based activity metabolomic platforms that can be used to analyze various fluxomics and for multivariant statistical analysis in cancer. We also aim to provide the reader with a basic understanding of NMR spectroscopy, cancer metabolomics, target profiling, chemometrics, and multifunctional tools for metabolomics discrimination, with a focus on metabolic phenotypic diversity for cancer therapeutics.     

Metabolic profiling in human lung injuries by high-resolution nuclear magnetic resonance spectroscopy of bronchoalveolar lavage fluid (BALF)

We present a method for identifying biomarkers in human lung injury. The method is based on high-resolution nuclear magnetic resonance (NMR) spectroscopy applied to bronchoalveolar lavage fluid (BALF) collected from lungs of critically ill patients. This biological fluid can be obtained by bronchoscopic and non-bronchoscopic methods. The type of lung injury in acute respiratory failure presenting as acute lung injury (ALI) and its severe form, acute respiratory distress syndrome (ARDS), continues to challenge critical care physicians. We characterize different metabolites in BAL fluid by non-bronchoscopic method (mBALF) for better diagnosis and understanding of ALI/ARDS by NMR spectroscopy. NMR spectra of mBALF collected from 30 patients (9 controls, 10 ARDS and 11 ALI) were analyzed for the identification of biomarkers. Statistical methods such as principal components analysis and partial least square discriminant analysis were carried out on ¹H NMR spectrum of mBALF to identify biomarker responsible for separation among different lung injuries classes (ALI and ARDS) and normal lungs. The corresponding correlation of biomarkers with metabolic cycle has given insight into metabolism of lung injuries in critically ill patients. Our study shows statistically significant differentiation of various metabolites concentration in mBALF collected from lungs of ALI, ARDS and healthy control patients, making NMR spectroscopy as a possible new method of characterizing human lung injury.     

A metabolic system-wide characterisation of the pig: a model for human physiology

The pig is a single-stomached omnivorous mammal and is an important model of human disease and nutrition. As such, it is necessary to establish a metabolic framework from which pathology-based variation can be compared. Here, a combination of one and two-dimensional (1)H and (13)C nuclear magnetic resonance spectroscopy (NMR) and high-resolution magic angle spinning (HR-MAS) NMR was used to provide a systems overview of porcine metabolism via characterisation of the urine, serum, liver and kidney metabolomes. The metabolites observed in each of these biological compartments were found to be qualitatively comparable to the metabolic signature of the same biological matrices in humans and rodents. The data were modelled using a combination of principal components analysis and Venn diagram mapping. Urine represented the most metabolically distinct biological compartment studied, with a relatively greater number of NMR detectable metabolites present, many of which are implicated in gut-microbial co-metabolic processes. The major inter-species differences observed were in the phase II conjugation of extra-genomic metabolites; the pig was observed to conjugate p-cresol, a gut microbial metabolite of tyrosine, with glucuronide rather than sulfate as seen in man. These observations are important to note when considering the translatability of experimental data derived from porcine models.