Use of NMR metabolomic plasma profiling methodologies to identify illicit growth-promoting administrations

Detection of growth-promoter use in animal production systems still proves to be an analytical challenge despite years of activity in the field. This study reports on the capability of NMR metabolomic profiling techniques to discriminate between plasma samples obtained from cattle treated with different groups of growth-promoting hormones (dexamethasone, prednisolone, oestradiol) based on recorded metabolite profiles. Two methods of NMR analysis were investigated—a Carr–Purcell–Meiboom–Gill (CPMG)-pulse sequence technique and a conventional ¹H NMR method using pre-extracted plasma. Using the CPMG method, 17 distinct metabolites could be identified from the spectra. ¹H NMR analysis of extracted plasma facilitated identification of 23 metabolites—six more than the alternative method and all within the aromatic region. Multivariate statistical analysis of acquired data from both forms of NMR analysis separated the plasma metabolite profiles into distinct sample cluster sets representative of the different animal study groups. Samples from both sets of corticosteroid-treated animals—dexamethasone and prednisolone—were found to be clustered relatively closely and had similar alterations to identified metabolite panels. Distinctive metabolite profiles, different from those observed within plasma from corticosteroid-treated animal plasma, were observed in oestradiol-treated animals and samples from these animals formed a cluster spatially isolated from control animal plasma samples. These findings suggest the potential use of NMR methodologies of plasma metabolite analysis as a high-throughput screening technique to aid detection of growth promoter use.     

Ex-vivo NMR of unprocessed tissue in water: a simplified procedure for studying intracranial neoplasms

Ex-vivo and in-vitro nuclear magnetic resonance (NMR) spectroscopy techniques have been used for studying chemical metabolites in surgically resected specimens of human neoplasms, and may provide complementary information to in-vivo whole-body magnetic-resonance spectroscopy (MRS). We describe an ex-vivo NMR in water method for measurement of water-soluble metabolites in unprocessed normal rat brain tissue and human intracranial neoplasms. The NMR spectra obtained using the method described here were comparable to those obtained using high-resolution magic-angle spinning (HRMAS) NMR methods, with good correlation in metabolite concentrations relative to creatine (r ² = 0.7635). Improved spectral resolution and baseline were noted compared to HRMAS, but macromolecule resonances were not detected. Ex-vivo NMR of unprocessed tissue in water is rapid and technically simple to perform, and has the potential to be used for direct assessment of intracranial neoplasms.     

An integrated strategy for in vivo metabolite profiling using high-resolution mass spectrometry based data processing techniques

An ongoing challenge of drug metabolite profiling is to detect and identify unknown or low-level metabolites in complex biological matrices. Here we present a generic strategy for metabolite detection using multiple accurate-mass-based data processing tools via the analysis of rat samples of two model drug candidates, AZD6280 and AZ12488024. First, the function of isotopic pattern recognition was proved to be highly effective in the detection of metabolites derived from [¹⁴C]-AZD6280 that possesses a distinct isotopic pattern. The metabolites revealed using this approach were in excellent qualitative correlation to those observed in radiochromatograms. Second, the effectiveness of accurate mass based untargeted data mining tools such as background subtraction, mass defect filtering, or a data mining package (MZmine) used for metabolomic analysis in detection of metabolites of [¹⁴C]-AZ12488024 in rat urine, feces, bile and plasma samples was examined and a total of 33 metabolites of AZ12488024 were detected. Among them, at least 16 metabolites were only detected by the aid of the data mining packages and not via radiochromatograms. New metabolic pathways such as S-oxidation and thiomethylation reactions occurring on the thiazole ring were proposed based on the processed data. The results of these experiments also demonstrated that accurate mass-based mass defect filtering (MDF) and data mining techniques used in metabolomics are complementary and can be valuable tools for delineating low-level metabolites in complex matrices. Furthermore, the application of distinct multiple data-mining algorithms in parallel, or in tandem, can be effective for rapidly profiling in vivo drug metabolites.     

Characterization of Brain Metabolism by Nuclear Magnetic Resonance

The noninvasive, quantitative ability of nuclear magnetic resonance (NMR) spectroscopy to characterize small molecule metabolites has long been recognized as a major strength of its application in biology. Numerous techniques exist for characterizing metabolism in living, excised, or extracted tissue, with a particular focus on ¹H-based methods due to the high sensitivity and natural abundance of protons. With the increasing use of high magnetic fields, the utility of in vivo ¹H magnetic resonance spectroscopy (MRS) has markedly improved for measuring specific metabolite concentrations in biological tissues. Higher fields, coupled with recent developments in hyperpolarization, also enable techniques for complimenting ¹H measurements with spectroscopy of other nuclei, such as ³¹P and ¹³C, and for combining measurements of metabolite pools with metabolic flux measurements. We compare ex vivo and in vivo methods for studying metabolism in the brain using NMR and highlight insights gained through using higher magnetic fields, the advent of dissolution dynamic nuclear polarization, and combining in vivo MRS and ex vivo NMR approaches.     

Application of HR-MAS NMR spectroscopy for studying chemotype variations of Withania somnifera (L.) Dunal

Withania somnifera (L.) Dunal (Solanaceae), commonly known as Ashwagandha, is one of the most valued Indian medicinal plants with a number of pharmaceutical and nutraceutical applications. Metabolic profiling has been performed by HR-MAS NMR spectroscopy on fresh leaf and root tissue specimens from four chemotypes of W. somnifera. The HR-MAS NMR spectroscopy of lyophilized defatted leaf tissue specimens clearly distinguishes resonances of medicinally important secondary metabolites (withaferin A and withanone) and its distinctive quantitative variability among the chemotypes. A total of 41 metabolites were identified from both the leaf and root tissues of the chemotypes. The presence of methanol in leaf and root tissues of W. somnifera was detected by HR-MAS NMR spectroscopy. Multivariate principal component analysis (PCA) on HR-MAS (1) H NMR spectra of leaves revealed clear variations in primary metabolites among the chemotypes. The results of the present study demonstrated an efficient method, which can be utilized for metabolite profiling of primary and secondary metabolites in medicinally important plants.     

Metabolite assignment of ultrafiltered synovial fluid extracted from knee joints of reactive arthritis patients using high resolution NMR spectroscopy

Currently, there are no reliable biomarkers available that can aid early differential diagnosis of reactive arthritis (ReA) from other inflammatory joint diseases. Metabolic profiling of synovial fluid (SF)—obtained from joints affected in ReA—holds great promise in this regard and will further aid monitoring treatment and improving our understanding about disease mechanism. As a first step in this direction, we report here the metabolite specific assignment of ¹H and ¹³C resonances detected in the NMR spectra of SF samples extracted from human patients with established ReA. The metabolite characterization has been carried out on both normal and ultrafiltered (deproteinized) SF samples of eight ReA patients (n = 8) using high-resolution (800 MHz) ¹H and ¹H─¹³C NMR spectroscopy methods such as one-dimensional ¹H CPMG and two-dimensional J-resolved¹H NMR and homonuclear ¹H─¹H TOCSY and heteronuclear¹H─¹³C HSQC correlation spectra. Compared with normal SF samples, several distinctive ¹H NMR signals were identified and assigned to metabolites in the ¹H NMR spectra of ultrafiltered SF samples. Overall, we assigned 53 metabolites in normal filtered SF and 64 metabolites in filtered pooled SF sample compared with nonfiltered SF samples for which only 48 metabolites (including lipid/membrane metabolites as well) have been identified. The established NMR characterization of SF metabolites will serve to guide future metabolomics studies aiming to identify/evaluate the SF-based metabolic biomarkers of diagnostic/prognostic potential or seeking biochemical insights into disease mechanisms in a clinical perspective.     

A Survey of Orbitrap All Ion Fragmentation Analysis Assessed by an R MetaboList Package to Study Small-Molecule Metabolites

Leukemia cell and melanoma tumor tissue extracts were studied for small (mostly m/z?<?250) polar metabolites by LC-ESI-HRMSⁿ analysis powered by a hybrid Quadrupole-Orbitrap. MS data were simultaneously acquired in fast polarity switching mode operating in MS¹ and MS/MS (All Ion Fragmentation, AIF) full-scan analyses at high mass resolution. Positive metabolite assignments were achieved by AIF analysis considering at least two characteristic transitions. Targeted metabolite profiling was achieved by the relative quantification of 18 metabolites through spiking of their respective deuterated counterparts. Manual data processing of MS¹ and AIF scans were compared for the accurate determination of natural metabolites and their deuterated analogs by chromatographic alignment and peak area integration. Evaluation of manual and automated (MetaboList R package) AIF data processing yielded comparable results. The versatility of AIF analysis also enabled the untargeted metabolite profiling of leukemia and melanoma samples in which 22 and 53 compounds were, respectively, identified outside those studied by labeling. The main limitation of this method was that low abundance metabolites with scan rates below 8 scans/peak could not be accurately quantified by AIF analysis. The combination of AIF analysis with MetaboList R package represents an opportunity to move towards automated, faster, and more global metabolomics approaches supported by an entirely flexible open source data processing platform freely available from Comprehensive R Archive Network (CRAN, https://CRAN.R-project.org/package=MetaboList).     

Monitoring of metabolic profiling and water status of Hayward kiwifruits by nuclear magnetic resonance

The metabolic profiling of kiwifruit (Actinidia deliciosa, Hayward cultivar) aqueous extracts and the water status of entire kiwifruits were monitored over the season (June-December) using nuclear magnetic resonance (NMR) methodologies. The metabolic profiling of aqueous kiwifruit extracts was investigated by means of high field NMR spectroscopy. A large number of water-soluble metabolites were assigned by means of 1D and 2D NMR experiments. The change in the metabolic profiles monitored over the season allowed the kiwifruit development to be investigated. Specific temporal trends of aminoacids, sugars, organic acids and other metabolites were observed.The water status of kiwifruits was monitored directly on the intact fruit measuring the T₂ spin-spin relaxation time by means of a portable unilateral NMR instrument, fully non-invasive. Again, clear trends of the relaxation time were observed during the monitoring period.The results show that the monitoring of the metabolic profiling and the monitoring of the water status are two complementary means suitable to have a complete view of the investigated fruit.     

Automated annotation and quantification of metabolites in <Superscript>1</Superscript>H NMR data of biological origin

In ¹H NMR metabolomic datasets, there are often over a thousand peaks per spectrum, many of which change position drastically between samples. Automatic alignment, annotation, and quantification of all the metabolites of interest in such datasets have not been feasible. In this work we propose a fully automated annotation and quantification procedure which requires annotation of metabolites only in a single spectrum. The reference database built from that single spectrum can be used for any number of ¹H NMR datasets with a similar matrix. The procedure is based on the generalized fuzzy Hough transform (GFHT) for alignment and on Principal-components analysis (PCA) for peak selection and quantification. We show that we can establish quantities of 21 metabolites in several ¹H NMR datasets and that the procedure is extendable to include any number of metabolites that can be identified in a single spectrum. The procedure speeds up the quantification of previously known metabolites and also returns a table containing the intensities and locations of all the peaks that were found and aligned but not assigned to a known metabolite. This enables both biopattern analysis of known metabolites and data mining for new potential biomarkers among the unknowns.     

Development of chemical isotope labeling liquid chromatography mass spectrometry for silkworm hemolymph metabolomics

Silkworm (Bombyx mori) is a very useful target insect for evaluation of endocrine disruptor chemicals (EDCs) due to mature breeding techniques, complete endocrine system and broad basic knowledge on developmental biology. Comparative metabolomics of silkworms with and without EDC exposure offers another dimension of studying EDCs. In this work, we report a workflow on metabolomic profiling of silkworm hemolymph based on high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) and demonstrate its application in studying the metabolic changes associated with the pesticide dichlorodiphenyltrichloroethane (DDT) exposure in silkworm. Hemolymph samples were taken from mature silkworms after growing on diet that contained DDT at four different concentrations (1, 0.1, 0.01, 0.001 ppm) as well as on diet without DDT as controls. They were subjected to differential ¹²C-/¹³C-dansyl labeling of the amine/phenol submetabolome, LC-UV quantification of the total amount of labeled metabolites for sample normalization, and LC-MS detection and relative quantification of individual metabolites in comparative samples. The total concentration of labeled metabolites did not show any significant change between four DDT-treatment groups and one control group. Multivariate statistical analysis of the metabolome data set showed that there was a distinct metabolomic separation between the five groups. Out of the 2044 detected peak pairs, 338 and 1471 metabolites have been putatively identified against the HMDB database and the EML library, respectively. 65 metabolites were identified by the dansyl library searching based on the accurate mass and retention time. Among the 65 identified metabolites, 33 positive metabolites had changes of greater than 1.20-fold or less than 0.83-fold in one or more groups with p-value of smaller than 0.05. Several useful biomarkers including serine, methionine, tryptophan, asymmetric dimethylarginine, N-Methyl-D-aspartic and tyrosine were identified. The changes of these biomarkers were likely due to the disruption of the endocrine system of silkworm by DDT. This work illustrates that the method of CIL LC-MS is useful to generate quantitative submetabolome profiles from a small volume of silkworm hemolymph with much higher coverage than conventional LC-MS methods, thereby facilitating the discovery of potential metabolite biomarkers related to EDC or other chemical exposure.     

A metabonomics investigation of multiple sclerosis by nuclear magnetic resonance

Multiple sclerosis (MS) is a nervous system disease that affects the fatty myelin sheaths around the axons of the brain and spinal cord, leading to demyelination and a broad range of signs and symptoms. MS can be difficult to diagnose because its signs and symptoms may be similar to other medical problems. To find out which metabolites in serum are effective for the diagnosis of MS, we utilized metabolic profiling using proton nuclear magnetic resonance spectroscopy (¹H‐NMR). Random forest (RF) was used to classify the MS patients and healthy subjects. Atomic absorption spectroscopy was used to measure the serum levels of selenium. The results showed that the levels of selenium were lower in the MS group, when compared with the control group. RF was used to identify the metabolites that caused selenium changes in people with MS by building a correlation model between these metabolites and serum levels of selenium. For the external test set, the obtained classification model showed a 93% correct classification of MS and healthy subjects. The regression model of levels of selenium and metabolites showed the correlation (R²) value of 0.88 for the external test set. The results indicate the suitability of NMR as a screen for identifying MS patients and healthy subjects. A novel model with good prediction outcomes was constructed between serum levels of selenium and NMR data. Copyright © 2012 John Wiley & Sons, Ltd.     

Effect of Different Drying Methods on the Nutritional Value of Hibiscus sabdariffa Calyces as Revealed by NMR Metabolomics

Red mature calyces of Hibiscus sabdariffa were collected from 16 different locations in Meghalaya, India. Samples were processed using shade drying (SD) and tray drying (TD). NMR spectroscopy was used to assess the metabolic composition of the calyces. In this study, 18 polar metabolites were assigned using 1D and 2D NMR spectra, and 10 of them were quantified. Proximate analysis showed that the TD method is more efficient at reducing moisture and maintaining the ash content of the Hibiscus biomass. NMR metabolomics indicates that the metabolite composition significantly differs between SD and TD samples and is more stable in TD plant processing. The differences in post-harvest drying has a greater impact on the metabolite composition of Hibiscus than the plant location.     

Metabolic Profiling of Human Colorectal Cancer Using High Resolution 1H Nuclear Magnetic Resonance Spectroscopy

Colorectal cancer (CRC) is the third commonest malignancy cancer worldwide. Clear understandings of global metabolic profiling of the normal mucosa and cancer tissues are vitally important to aid optimizing the clinical management strategy and understanding CRC biology. We studied metabolic characteristics of 20 CRC and 20 distant normal mucosa tissues extracts from 20 patients using high resolution ¹H NMR spectroscopy in conjunction with multivariate analyses, such as principal component analysis (PCA). Compared with distant normal mucosa tissues, lactate, taurine, ornithine and polyamine were present at significantly higher levels in CRC tissue extracts whereas myo‐inositol was present at significantly lower level. Two metabolites ratios such as myo‐inositol/taurine and myo‐inositol/(ornithine+polyamine) appear to be the most valuable biomarkers for the differentiation CRC from normal mucosa tissues. Our data suggested that HR ¹H NMR spectroscopy combined with multivariate analyses is a potentially useful technology for detecting malignant changes in the normal mucosa tissues, the technique may be further exploited for future CRC biomarker research or identification of targets for therapeutic manipulations.     

Metabonomic profiling of renal cell carcinoma: high-resolution proton nuclear magnetic resonance spectroscopy of human serum with multivariate data analysis

Metabonomic profiling using proton nuclear magnetic resonance (¹H NMR) spectroscopy and multivariate data analysis of human serum samples was used to characterize metabolic profiles in renal cell carcinoma (RCC). We found distinct, easily detectable differences between (a) RCC patients and healthy humans, (b) RCC patients with metastases and without metastases, and (c) RCC patients before and after nephrectomy. Compared to healthy human serum, RCC serum had higher levels of lipid (mainly very low-density lipoproteins), isoleucine, leucine, lactate, alanine, N-acetylglycoproteins, pyruvate, glycerol, and unsaturated lipid, together with lower levels of acetoacetate, glutamine, phosphatidylcholine/choline, trimethylamine-N-oxide, and glucose. This pattern was somewhat reversed after nephrectomy. Altered metabolite concentrations are most likely the result of the cells switching to glycolysis to maintain energy homeostasis following the loss of ATP caused by impaired TCA cycle in RCC. Serum NMR spectra combined with principal component analysis techniques offer an efficient, convenient way of depicting tumour biochemistry and stratifying tumours under different pathophysiological conditions. It may be able to assist early diagnosis and postoperative surveillance of human malignant diseases using single blood samples.     

Metabolomics of meat exudate: Its potential to evaluate beef meat conservation and aging

In this study we analyzed the exudate of beef to evaluate its potential as non invasive sampling for nuclear magnetic resonance (NMR) based metabolomic analysis of meat samples. Exudate, as the natural juice from raw meat, is an easy to obtain matrix that it is usually collected in small amounts in commercial meat packages. Although meat exudate could provide complete and homogeneous metabolic information about the whole meat piece, this sample has been poorly studied. Exudates from 48 beef samples of different breeds, cattle and storage times have been studied by ¹H NMR spectroscopy. The liquid exudate spectra were compared with those obtained by High Resolution Magic Angle Spinning (HRMAS) of the original meat pieces. The close correlation found between both spectra (>95% of coincident peaks in both registers; Spearman correlation coefficient = 0.945) lead us to propose the exudate as an excellent alternative analytical matrix with a view to apply meat metabolomics. 60 metabolites could be identified through the analysis of mono and bidimensional exudate spectra, 23 of them for the first time in NMR meat studies. The application of chemometric tools to analyze exudate dataset has revealed significant metabolite variations associated with meat aging. Hence, NMR based metabolomics have made it possible both to classify meat samples according to their storage time through Principal Component Analysis (PCA), and to predict that storage time through Partial Least Squares (PLS) regression.     

Bioanalytical methods for metabolomic profiling: Detection of head and neck cancer,including oral cancer

Metabolomics is an emerging field dealing with the measurement and interpretation of small molecular byproducts of biochemical processes, or metabolites, which can be used to generate profiles from biological samples. Promising for use in pathophysiology, metabolomic profiles give the immediate biological state of a sample. These profiles are altered in diseases and are detectable in biological samples, such as tissue, blood, urine, saliva, and others. Most remarkably, metabolic profiles usually are altered before symptoms appear in a patient. For this reason, metabolomics has potential as a reliable method for an early diagnosis of diseases through disease biomarker identification. This application is most prevalent in cancer, such as head and neck cancer (HNC). Metabolomic studies offer avenues to improve on current medical techniques through the application of mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and statistical analysis to determine better biomarkers than those currently known. In this review, we discuss the use of MS and NMR tools for detecting biomarkers in tissue and fluid samples, and the appropriateness of metabolomics in analyzing cancer. Advantages, disadvantages, and recent studies on metabolomic profiling techniques in HNC analysis are also discussed herein.     

Strategy for choosing extraction procedures for NMR-based metabolomic analysis of mammalian cells

Metabolomic analysis of mammalian cells can be applied across multiple fields including medicine and toxicology. It requires the acquisition of reproducible, robust, reliable, and homogeneous biological data sets. Particular attention must be paid to the efficiency and reliability of the extraction procedure. Even though a number of recent studies have dealt with optimizing a particular protocol for specific matrices and analytical techniques, there is no universal method to allow the detection of the entire cellular metabolome. Here, we present a strategy for choosing extraction procedures from adherent mammalian cells for the global NMR analysis of the metabolome. After the quenching of cells, intracellular metabolites are extracted from the cells using one of the following solvent systems of varying polarities: perchloric acid, acetonitrile/water, methanol, methanol/water, and methanol/chloroform/water. The hydrophilic metabolite profiles are analysed using ¹H nuclear magnetic resonance (NMR) spectroscopy. We propose an original geometric representation of metabolites reflecting the efficiency of extraction methods. In the case of NMR-based analysis of mammalian cells, this methodology demonstrates that a higher portion of intracellular metabolites are extracted by using methanol or methanol/chloroform/water. The preferred method is evaluated in terms of biological variability for studying metabolic changes caused by the phenotype of four different human breast cancer cell lines, showing that the selected extraction procedure is a promising tool for metabolomic and metabonomic studies of mammalian cells. The strategy proposed in this paper to compare extraction procedures is applicable to NMR-based metabolomic studies of various systems.     

Targeted projection NMR spectroscopy for unambiguous metabolic profiling of complex mixtures

Unambiguous identification of individual metabolites present in complex mixtures such as biofluids constitutes a crucial prerequisite for quantitative metabolomics, toward better understanding of biochemical processes in living systems. Increasing the dimensionality of a given NMR correlation experiment is the natural solution for resolving spectral overlap. However, in the context of metabolites, natural abundance acquisition of ¹H and ¹³C NMR data virtually excludes the use of higher dimensional NMR experiments (3D, 4D, etc.) that would require unrealistically long acquisition times. Here, we introduce projection NMR techniques for studies of complex mixtures, and we show how discrete sets of projection spectra from higher dimensional NMR experiments are obtained in a reasonable time frame, in order to capture essential information necessary to resolve assignment ambiguities caused by signal overlap in conventional 2D NMR spectra. We determine optimal projection angles where given metabolite resonances will have the least overlap, to obtain distinct metabolite assignment in complex mixtures. The method is demonstrated for a model mixture composition made of ornithine, putrescine and arginine for which acquisition of a single 2D projection of a 3D ¹H–¹³C TOCSY‐HSQC spectrum allows to disentangle the metabolite signals and to access to complete profiling of this model mixture in the targeted 2D projection plane. Copyright © 2010 John Wiley & Sons, Ltd.     

Carbon-13 Nuclear Magnetic Resonance Spectroscopy Study of the Glucose Stress of Human Lenses

The sorbitol theory in diabetic cataractogenesis was based on sorbitol accumulation under glucose stress. Sorbitol accumulation was examined by ¹³C nuclear magnetic resonance spectroscopy (NMR) for the first time in matched human lenses incubated in 5.5 mM and 35.5 mM C-1 ¹³C-enriched glucose up to 28 hours. The results showed that sorbitol and lactate in human tens can be detected at 35.5 mM, but not in 5.5 mM glucose solution. The glycolysis metabolic pathway of human lenses may be quite different from that of animals. The accumulation of metabolites can be traced and quantified by the intensities of ¹³C NMR peaks. Therefore, ¹³C NMR spectroscopy can be used as a valuable tool to investigate human lens carbohydrate metabolism non-interventionally.     

Complexation of zearalenone and zearalenols with native and modified β-cyclodextrins

The complexation of the oestrogenic mycotoxin zearalenone (ZEN) and its metabolites α- and β-zearalenol (ZOLs) with native β-cyclodextrins (β-CD) and modified hydroxypropyl-β-CD and dimethyl-β-CD was studied by fluorescence spectroscopy, nuclear magnetic resonance spectroscopy and electrospray-mass spectrometry. The formation of the inclusion complex was confirmed by NMR studies of zearalenone:β-CD solution. NMR, ESI-MS and fluorescence data are in agreement with the formation of a 1:1 complex between zearalenone and β-CD, characterized by the deep insertion of the phenolic moiety inside the cavity of the CD from its secondary side. The complexes formed between the guests and native β-CD are characterized by high stability constants (>10⁴), as measured by fluorescence titrations.