Evaluating low-intensity unknown signals in quantitative proton NMR mixture analysis

Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods

To address the growing concern of honey adulteration in Canada and globally, a quantitative NMR method was developed to analyze 424 honey samples collected across Canada as part of two surveys in 2018 and 2019 led by the Canadian Food Inspection Agency. Based on a robust and reproducible methodology, NMR data were recorded in triplicate on a 700 MHz NMR spectrometer equipped with a cryoprobe, and the data analysis led to the identification and quantification of 33 compounds characteristic of the chemical composition of honey. The high proportion of Canadian honey in the library provided a unique opportunity to apply multivariate statistical methods including PCA, PLS-DA, and SIMCA in order to differentiate Canadian samples from the rest of the world. Through satisfactory model validation, both PLS-DA as a discriminant modeling technique and SIMCA as a class modeling method proved to be reliable at differentiating Canadian honey from a diverse set of honeys with various countries of origins and floral types. The replacement method of optimization was successfully applied for variable selection, and trigonelline, proline, and ethanol at a lower extent were identified as potential chemical markers for the discrimination of Canadian and non-Canadian honeys.

Ethanol Biofuel Cells: Hybrid Catalytic Cascades as a Tool for Biosensor Devices

Biofuel cells use chemical reactions and biological catalysts (enzymes or microorganisms) to produce electrical energy, providing clean and renewable energy. Enzymatic biofuel cells (EBFCs) have promising characteristics and potential applications as an alternative energy source for low-power electronic devices. Over the last decade, researchers have focused on enhancing the electrocatalytic activity of biosystems and on increasing energy generation and electronic conductivity. Self-powered biosensors can use EBFCs while eliminating the need for an external power source. This review details improvements in EBFC and catalyst arrangements that will help to achieve complete substrate oxidation and to increase the number of collected electrons. It also describes how analytical techniques can be employed to follow the intermediates between the enzymes within the enzymatic cascade. We aim to demonstrate how a high-performance self-powered sensor design based on EBFCs developed for ethanol detection can be adapted and implemented in power devices for biosensing applications.

NMR Spectroscopy-Based Metabolic Profiling of Biospecimens

Metabolomics refers to study of metabolites in biospecimens such as blood serum, tissues, and urine. Nuclear magnetic resonance (NMR) spectroscopy and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS; mass spectrometry coupled with liquid chromatography) are most frequently employed to analyze complex biological/clinical samples. NMR is a relatively insensitive tool compared to UPLC-MS/MS but offers straightforward quantification and identification and easy sample processing. One-dimensional ¹ H NMR spectroscopy is inherently quantitative and can be readily used for metabolite quantification without individual metabolite standards. Two-dimensional spectroscopy is most commonly used for identification of metabolites but can also be used quantitatively. Although NMR experiments are unbiased regarding the chemical nature of the analyte, it is crucial to adhere to the proper metabolite extraction protocol for optimum results. Selection and implementation of appropriate NMR pulse programs are also important. Finally, employment of the correct metabolite quantification strategy is crucial as well. In this unit, step-by-step guidance for running an NMR metabolomics experiment from typical biospecimens is presented. The unit describes an optimized metabolite extraction protocol, followed by implementation of NMR experiments and quantification strategies using the so-called "targeted profiling" technique. This approach relies on an underlying basis set of metabolite spectra acquired under similar conditions. Some strategies for statistical analysis of the data are also presented. Overall, this set of protocols should serve as a guide for anyone who wishes to enter the world of NMR-based metabolomics analysis. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Metabolite extraction from different biospecimens Basic Protocol 2: Preparation of dried upper fraction for NMR analysis Alternate Protocol: Preparation of urine samples for NMR analysis Basic Protocol 3: NMR experiments Basic Protocol 4: Spectral processing and quantification of metabolites Basic Protocol 5: Statistical analysis of the data.

Urinary metabolites as noninvasive biomarkers of gastrointestinal diseases: A clinical review

The diagnosis of gastrointestinal (GI) disorders is usually based on invasive techniques such as endoscopy. A key important factor in GI cancer is early diagnosis which warrants development of non- or less-invasive diagnostic techniques. In addition, monitoring and surveillance are other important parts in the management of GI diseases. Metabolomics studies with nuclear magnetic resonance and mass spectrometry can measure the concentration of more than 3000 chemical compounds in the urine providing possible chemical signature in different diseases and during health. In this review, we discuss the urinary metabolomics signature of different GI diseases including GI cancer and elaborate on how these biomarkers could be used for the classification, early diagnosis and the monitoring of the patients. Moreover, we discuss future directions of this still evolving field of research.

SENSI: signal enhancement by spectral integration for the analysis of metabolic mixtures

The method provided here can overcome the low S/N problem in ¹³C NMR by the integration of plural spectra to increase the resolution based on non-bucketing analysis without measurements.

Metabolomic analysis reveals extended metabolic consequences of marginal vitamin B-6 deficiency in healthy human subjects

Marginal deficiency of vitamin B-6 is common among segments of the population worldwide. Because pyridoxal 5′-phosphate (PLP) serves as a coenzyme in the metabolism of amino acids, carbohydrates, organic acids, and neurotransmitters, as well as in aspects of one-carbon metabolism, vitamin B-6 deficiency could have many effects. Healthy men and women (age: 20-40 y; n = 23) were fed a 2-day controlled, nutritionally adequate diet followed by a 28-day low-vitamin B-6 diet (<0.5 mg/d) to induce marginal deficiency, as reflected by a decline of plasma PLP from 52.6±14.1 (mean ± SD) to 21.5±4.6 nmol/L (P<0.0001) and increased cystathionine from 131±65 to 199±56 nmol/L (P<0.001). Fasting plasma samples obtained before and after vitamin B6 restriction were analyzed by ¹H-NMR with and without filtration and by targeted quantitative analysis by mass spectrometry (MS). Multilevel partial least squares-discriminant analysis and S-plots of NMR spectra showed that NMR is effective in classifying samples according to vitamin B-6 status and identified discriminating features. NMR spectral features of selected metabolites indicated that vitamin B-6 restriction significantly increased the ratios of glutamine/glutamate and 2-oxoglutarate/glutamate (P<0.001) and tended to increase concentrations of acetate, pyruvate, and trimethylamine-N-oxide (adjusted P<0.05). Tandem MS showed significantly greater plasma proline after vitamin B-6 restriction (adjusted P<0.05), but there were no effects on the profile of 14 other amino acids and 45 acylcarnitines. These findings demonstrate that marginal vitamin B-6 deficiency has widespread metabolic perturbations and illustrate the utility of metabolomics in evaluating complex effects of altered vitamin B-6 intake.

Novel monosaccharide fermentation products in Caldicellulosiruptor saccharolyticus identified using NMR spectroscopy

BACKGROUND

Caldicellulosiruptor saccharolyticus is a thermophilic, Gram-positive, non-spore forming, strictly anaerobic bacterium of interest in potential industrial applications, including the production of biofuels such as hydrogen or ethanol from lignocellulosic biomass through fermentation. High-resolution, solution-state nuclear magnetic resonance (NMR) spectroscopy is a useful method for the identification and quantification of metabolites that result from growth on different substrates. NMR allows facile resolution of isomeric (identical mass) constituents and does not destroy the sample.

RESULTS

Profiles of metabolites produced by the thermophilic cellulose-degrading bacterium Caldicellulosiruptor saccharolyticus DSM 8903 strain following growth on different monosaccharides (D-glucose, D-mannose, L-arabinose, D-arabinose, D-xylose, L-fucose, and D-fucose) as carbon sources revealed several unexpected fermentation products, suggesting novel metabolic capacities and unexplored metabolic pathways in this organism. Both 1H and 13C nuclear magnetic resonance (NMR) spectroscopy were used to determine intracellular and extracellular metabolite profiles. One dimensional 1H NMR spectral analysis was performed by curve fitting against spectral libraries provided in the Chenomx software; 2-D homonuclear and heteronuclear NMR experiments were conducted to further reduce uncertainties due to unassigned, overlapping, or poorly-resolved peaks. In addition to expected metabolites such as acetate, lactate, glycerol, and ethanol, several novel fermentation products were identified: ethylene glycol (from growth on D-arabinose), acetoin and 2,3-butanediol (from growth on D-glucose, L-arabinose, and D-xylose), and hydroxyacetone (from growth on D-mannose, L-arabinose, and D-xylose). Production of ethylene glycol from D-arabinose was particularly notable, with around 10% of the substrate carbon converted into this uncommon fermentation product.

CONCLUSIONS

The present research shows that C. saccharolyticus, already of substantial interest due to its capability for biological ethanol and hydrogen production, has further metabolic potential for production of higher molecular weight compounds, such as acetoin and 2,3-butanediol, as well as hydroxyacetone and the uncommon fermentation product ethylene glycol. In addition, application of nuclear magnetic resonance (NMR) spectroscopy facilitates identification of novel metabolites, which is instrumental for production of desirable bioproducts from biomass through microbial fermentation.

Quantitative 1H NMR. Development and potential of an analytical method: an update

Covering the literature from mid-2004 until the end of 2011, this review continues a previous literature overview on quantitative (1)H NMR (qHNMR) methodology and its applications in the analysis of natural products. Among the foremost advantages of qHNMR is its accurate function with external calibration, the lack of any requirement for identical reference materials, a high precision and accuracy when properly validated, and an ability to quantitate multiple analytes simultaneously. As a result of the inclusion of over 170 new references, this updated review summarizes a wealth of detailed experiential evidence and newly developed methodology that supports qHNMR as a valuable and unbiased analytical tool for natural product and other areas of research.

Integrated standardization concept for Angelica botanicals using quantitative NMR

Despite numerous in vitro/vivo and phytochemical studies, the active constituents of Angelica sinensis (AS) have not been conclusively identified for the standardization to bioactive markers. Phytochemical analyses of AS extracts and fractions that demonstrate activity in a panel of in vitro bioassays, have repeatedly pointed to ligustilide as being (associated with) the active principle(s). Due to the chemical instability of ligustilide and related issues in GC/LC analyses, new methods capable of quantifying ligustilide in mixtures that do not rely on an identical reference standard are in high demand. This study demonstrates how NMR can satisfy the requirement for simultaneous, multi-target quantification and qualitative identification. First, the AS activity was concentrated into a single fraction by RP-solid-phase extraction, as confirmed by an alkaline phosphatase, (anti-)estrogenicity and cytotoxicity assay. Next, a quantitative (1)H NMR (qHNMR) method was established and validated using standard compounds and comparing processing methods. Subsequent 1D/2D NMR and qHNMR analysis led to the identification and quantification of ligustilide and other minor components in the active fraction, and to the development of quality criteria for authentic AS preparations. The absolute and relative quantities of ligustilide, six minor alkyl phthalides, and groups of phenylpropanoids, polyynes, and poly-unsaturated fatty acids were measured by a combination of qHNMR and 2D COSY. The qNMR approach enables multi-target quality control of the bioactive fraction, and enables the integrated biological and chemical standardization of AS botanicals. This methodology can potentially be transferred to other botanicals with active principles that act synergistically, or that contain closely related and/or constituents, which have not been conclusively identified as the active principles.

(1)H NMR metabolomics identification of markers of hypoxia-induced metabolic shifts in a breast cancer model system

Hypoxia can promote invasive behavior in cancer cells and alters the response to therapeutic intervention as a result of changes in the expression many genes, including genes involved in intermediary metabolism. Although metabolomics technologies are capable of simultaneously measuring a wide range of metabolites in an untargeted manner, these methods have been relatively under utilized in the study of cancer cell responses to hypoxia. Thus, (1)H NMR metabolomics was used to examine the effects of hypoxia in the MDA-MB-231 human breast cancer cell line, both in vitro and in vivo. Cell cultures were compared with respect to their metabolic responses during growth under either hypoxic (1% O(2)) or normoxic conditions. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify a set of metabolites that were responsive to hypoxia. Via intracardiac administration, MDA-MB-231 cells were also used to generate widespread metastatic disease in immuno-compromised mice. Serum metabolite analysis was conducted to compare animals with and without a large tumor burden. Intriguingly, using a cross-plot of the OPLS loadings, both the in vitro and in vivo samples yielded a subset of metabolites that were significantly altered by hypoxia. These included primarily energy metabolites and amino acids, indicative of known alterations in energy metabolism, and possibly protein synthesis or catabolism. The results suggest that the metabolite pattern identified might prove useful as a marker for intra-tumoral hypoxia.

Profiling polar and semipolar plant metabolites throughout extraction processes using a combined solution-state and high-resolution magic angle spinning NMR approach

In metabolomic analyses, care should be exercised as to which metabolites are extracted from the sample and which remain in the residue; the remaining metabolites are typically discarded following the extraction process. In this study, nuclear magnetic resonance (NMR)-based metabolomics was used to visualize plant metabolite profiles throughout a series of repeated extraction processes. Metabolites remaining in the extraction residues of (13)C-labeled Arabidopsis thaliana were recovered by repeated extraction using methanol-d(4) and deuterium oxide. The soluble extracts and residual pellets from each step of the extraction process were analyzed by both solution-state and high-resolution magic angle spinning NMR. Metabolic profiling based on chemical shifts in two-dimensional (1)H-(13)C heteronuclear single-quantum coherence spectra allowed the elucidation of both structural and chemical properties. In addition to the metabolite profile, there appears to be a relationship between metabolite structure and behavior throughout the repeated extraction process. These approaches suggest that metabolites are not always extracted in a single step and that the distribution of metabolites in an extraction scenario cannot be predicted solely on the basis of solubility or polarity. The composition of all metabolites in cells influences the solubility of each metabolite; thus, particular attention should be paid because changes in only a portion of the metabolites could influence the entire metabolite profile in a solvent extract.

Statistical indices for simultaneous large-scale metabolite detections for a single NMR spectrum

NMR-based metabolomics has become a practical and analytical methodology for discovering novel genes, biomarkers, metabolic phenotypes, and dynamic cell behaviors in organisms. Recent developments in NMR-based metabolomics, however, have not concentrated on improvements of comprehensiveness in terms of simultaneous large-scale metabolite detections. To resolve this, we have devised and implemented a statistical index, the SpinAssign p-value, in NMR-based metabolomics for large-scale metabolite annotation and publicized this information. It enables simultaneous annotation of more than 200 candidate metabolites from the single (13)C-HSQC (heteronuclear single quantum coherence) NMR spectrum of a single sample of cell extract.

Analytical and statistical approaches to metabolomics research

Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern-recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.