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During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

Nicotinamide adenine dinucleotide (NAD+) and its reduced form (NADH) are coenzymes employed in hundreds of metabolic reactions. NAD+ also serves as a substrate for enzymes such as sirtuins, poly(ADP‐ribose) polymerases (PARPs) and ADP‐ribosyl cyclases. Given the pivotal role of NAD(H) in health and disease, studying NAD+ metabolism has become essential to monitor ge-netic‐ and/or drug‐induced perturbations related to metabolic status and diseases (such as ageing, cancer or obesity), and its possible therapies. Here, we present a strategy based on liquid chroma-tography‐tandem mass spectrometry (LC‐MS/MS), for the analysis of the NAD+ metabolome in biological samples. In this method, hydrophilic interaction chromatography (HILIC) was used to sep-arate a total of 18 metabolites belonging to pathways leading to NAD+ biosynthesis, including pre-cursors, intermediates and catabolites. As redox cofactors are known for their instability, a sample preparation procedure was developed to handle a variety of biological matrices: cell models, rodent tissues and biofluids, as well as human biofluids (urine, plasma, serum, whole blood). For clinical applications, quantitative LC‐MS/MS for a subset of metabolites was demonstrated for the analysis of the human whole blood of nine volunteers. Using this developed workflow, our methodology allows studying NAD+ biology from mechanistic to clinical applications.

Objective A decay in intracellular NAD+ levels is one of the hallmarks of physiological decline in normal tissue functions. Accordingly, dietary supplementation with NAD+ precursors can prevent, alleviate, or even reverse multiple metabolic complications and age-related disorders in diverse model organisms. Within the constellation of NAD+ precursors, nicotinamide riboside (NR) has gained attention due to its potent NAD+ biosynthetic effects in vivo while lacking adverse clinical effects. Nevertheless, NR is not stable in circulation, and its utilization is rate-limited by the expression of nicotinamide riboside kinases (NRKs). Therefore, there is a strong interest in identifying new effective NAD+ precursors that can overcome these limitations. Methods Through a combination of metabolomics and pharmacological approaches, we describe how NRH, a reduced form of NR, serves as a potent NAD+ precursor in mammalian cells and mice. Results NRH acts as a more potent and faster NAD+ precursor than NR in mammalian cells and tissues. Despite the minor structural difference, we found that NRH uses different steps and enzymes to synthesize NAD+, thus revealing a new NRK1-independent pathway for NAD+ synthesis. Finally, we provide evidence that NRH is orally bioavailable in mice and prevents cisplatin-induced acute kidney injury. Conclusions Our data identify a new pathway for NAD+ synthesis and classify NRH as a promising new therapeutic strategy to enhance NAD+ levels., Highlights • A reduced form of nicotinamide riboside (NRH) is a potent NAD+ precursor in cultured cells and mouse tissues. • NRH leads to NAD+ synthesis through a new, independent path to that of NR. • NRH is orally bioavailable and not degraded in plasma. • NRH alleviates cisplatin-induced acute kidney injury.

In Figure 4, the panels showing the variability by cycle phases for Glycine, Serine, Methionine, Asparagine, Proline, Glutamine, Tyrosine, Gamma-glutamyl-alanine, Citrulline, O-Acetyl-serine, Alpha-aminobutyric acid and Gamma-glutamylglutamine were omitted. The correct Figure 4 appears below as Figure 1.

Scope: Quinic acid in its free form is broadly abundant in plants, and can accumulate in copious amounts in coffee, tea, and certain fruits. However, it has been mostly studied as chlorogenic acid, an ester of caffeic and quinic acids. When chlorogenic acid reaches the colon, it is hydrolyzed by microbial esterases releasing caffeic and quinic acids. While biotransformation of chlorogenic and caffeic acids have been elucidated by in vitro and in vivo studies, the gut metabolism of quinic acid has been so far overlooked. Methods and Results: [U-13C]-Quinic acid is submitted to a colonic model using human fecal microbiota for assessing its metabolic fate. The metabolite profiles formed along microbial biotransformation are monitored by a combined metabolomics approach, using both 2D GC– and ultra-HPLC–MS. Six metabolic intermediates are identified by incorporation of isotopic label. Conclusion: Two parallel degradation pathways could be proposed: (1) an oxidative route, leading to aromatization and accumulation of protocatechuic acid, and a (2) reductive route, including dehydroxylation to cyclohexane carboxylic acid. Elucidating the biotransformation of food bioactives by the gut microbiota is of relevance for understanding nutrition, interindividual variability and potential effects on human metabolism.

Plant secondary metabolites are an almost unlimited reservoir of potential bioactive compounds. In view of the wide chemical space covered by natural compounds, their comprehensive analysis requires multiple and complementary approaches. In this study, numerous chromatographic conditions were tested for the analysis of a set of 120 representative natural compounds covering a wide polarity range (18 log P units). The experiments were performed on 59 different conditions involving 29 RPLC and HILIC dedicated stationary phases, as well as more exotic mixed mode columns. The best RPLC and HILIC conditions were determined using Derringer's desirability functions, based on various criteria (i.e. retention, peak shape, distribution of compounds during the gradient…). After this first selection, only the most promising conditions were kept (19 in RPLC and 11 in HILIC). The selectivity complementarity among each chromatographic mode was assessed by principal component analysis (PCA) and hierarchical cluster analysis (HCA). In RPLC, a pentabromobenzyl (PBrBz) stationary phase was identified as particularly versatile and could constitute an elegant first intention screening column. Two additional conditions allowed to extend the range of natural compounds space that can be analyzed, while offering better selectivity for basic analytes (hybrid silica graft with C8 moiety operated at pH 9 (Hyb C8)) and acidic compounds (positively charged hybrid silica graft with pentafluorophenyl moiety (Hyb+ PFPh). Although less generic in terms of amenable compounds, an ion exchange/RP mixed mode stationary phase (MM TriP1) offered notably enhanced retention of more polar analytes under RPLC conditions. With these four conditions, 89% of the natural substances were detected by LC-MS with acceptable retentions and peak shapes. In HILIC, four acceptable and complementary conditions were also highlighted. Both Syncro-Z (zwitterionic HILIC phase) and Diol columns were found to offer balanced retention and selectivity for most of the polar compounds (log DpH3

Health is influenced by genetic, lifestyle, and diet determinants; therefore, nutrition plays an essential role in health management. Still, the substantiation of nutritional health benefits is challenged by the intrinsic macro- and micronutrient complexity of foods and individual responses. Evidence of healthy effects of food requires new strategies not only to stratify populations according to their metabolic requirements but also to predict and measure individual responses to dietary intakes. The influence of the gut microbiome and its interaction with the host is pivotal to understand nutrition and metabolism. Thus, the modulation of the gut microbiome composition by alteration of food habits has potentialities in health improvement or even disease prevention. Dietary polyphenols are naturally occurring constituents in vegetables and fruits, including coffee and cocoa. They are commonly associated to health benefits, although mechanistic evidence in vivo is not yet fully understood. Polyphenols are extensively metabolized by gut bacteria into a complex series of end-products that support a significant effect on the functional ecology of symbiotic partners that can affect the host physiology. This review reports recent nutritional metabolomics inspections of gut microbiota-host metabolic interactions with a particular focus on the cometabolism of cocoa and coffee polyphenols.

Secondary metabolites are an almost unlimited reservoir of potential bioactive compounds. In view of the wide chemical space covered by natural compounds, their comprehensive analysis requires multiple cutting-edge approaches. This study evaluates the applicability of ultra-high performance supercritical fluid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UHPSFC-QqToF-MS) as an analytical strategy for plant metabolites profiling. Versatility of this analytical platform was first assessed using 120 highly diverse natural compounds (according to lipophilicity, hydrogen bond capability, acid-base properties, molecular mass and chemical structure) that were screened on a set of 15 rationally chosen stationary phase chemistries. UHPSFC-QqToF-MS provides a suitable analytical solution for 88% of the tested compounds. Three stationary phases (Diol, not endcapped C18 and 2-EP) were highlighted as particularly polyvalent, since they allow suitable elution of 101 out of 120 natural compounds. The systematic evaluation of retention and selectivity of natural compounds further underlined the suitability of these three columns for the separation of natural compounds. This reduced set of key stationary phases constitutes a basis for untargeted scouting analysis and method development. Even if they were less versatile, stationary phases such as endcapped T3C18, polar P-PFP, were nevertheless found to provide extended selectivity for specific natural molecules sub-classes. Finally, the identified polyvalent conditions were successfully applied for the analysis of complex polar and non-polar plant extracts. These first experimental hits demonstrate the full applicability and potential of UHPSFC-QqToF-MS for plant metabolite profiling.

Metabolomics studies rely on the analysis of the multitude of small molecules (metabolites) present in a biological system. Most commonly, metabolomics is heavily supported by mass spectrometry (MS) and nuclear magnetic resonance (NMR) as parallel technologies that provide an overview of the metabolome and high-power compound elucidation. Over and above large-scale analysis, a major effort is needed for unequivocal identification of metabolites. The combination of liquid chromatography (LC)-MS and NMR is a powerful methodology for identifying metabolites. Better chemical characterization of the metabolome will undoubtedly enlarge knowledge of any biological system.

The improvements in separation and analysis of complex mixtures by LC-NMR during the last decade have shifted its emphasis from data acquisition to data analysis. For correct data analysis, not only high quality datasets are necessary, but adequate software and adequate databases for semi (or fully)-automated assignments of complex molecules are needed. Only by using NMR, when necessary in combination with MS, the identification of molecules, as present for example in natural products, can be achieved. Here we report on the ongoing efforts required for the construction of an NMR database of flavonoids, implemented for automated assignments of flavonoids. The procedure is demonstrated for a series of flavonoids.

Food is an integral part of human life, and the composition of our diet is an important determinant of our health and well-being. Food is also the main source of energy and nutrients for the gut microbiota, the 100 trillion cells that coexist inside us. The impact of macronutrients (protein, fat, carbohydrates, and fiber) and specific non-nutrient food components (polyphenols) will be reviewed in the context of gut microbial function and interaction with the host. Colonic microbiota provides diverse enzymatic activities differing from our own, which lead to the production of metabolites essential for key metabolic functions, including carbohydrate and amino acid metabolism. Certain gut metabolites are specific to microbial activity and confer functionalities beyond energy production, such as signalling cascades across cells, tissues, and organs. Metabolomics has proven to be a useful tool to measure the effects of food on the gut microbiota and its interaction with host metabolism.

Identification of natural compounds, especially secondary metabolites, has been hampered by the lack of easy to use and accessible reference databases. Nuclear magnetic resonance (NMR) spectroscopy is the most selective technique for identification of unknown metabolites. High quality (1)H NMR (proton nuclear magnetic resonance) spectra combined with elemental composition obtained from mass spectrometry (MS) are essential for the identification process. Here, we present MetIDB, a reference database of experimental and predicted (1)H NMR spectra of 6000 flavonoids. By incorporating the stereochemistry, intramolecular interactions, and solvent effects into the prediction model, chemical shifts and couplings were predicted with great accuracy. A user-friendly web-based interface for MetIDB has been established providing various interfaces to the data and data-mining possibilities. For each compound, additional information is available comprising compound annotation, a (1)H NMR spectrum, 2D and 3D structure with correct stereochemistry, and monoisotopic mass as well as links to other web resources. The combination of chemical formula and (1)H NMR chemical shifts proved to be very efficient in metabolite identification, especially for isobaric compounds. Using this database, the process of flavonoid identification can then be significantly shortened by avoiding repetitive elucidation of already described compounds.

Systems biology approaches are providing novel insights into the role of nutrition for the management of health and disease. In the present study, we investigated if dietary preference for dark chocolate in healthy subjects may lead to different metabolic response to daily chocolate consumption. Using NMR- and MS-based metabolic profiling of blood plasma and urine, we monitored the metabolic response of 10 participants stratified as chocolate desiring and eating regularly dark chocolate (CD) and 10 participants stratified as chocolate indifferent and eating rarely dark chocolate (CI) to a daily consumption of 50 g of dark chocolate as part of a standardized diet over a one week period. We demonstrated that preference for chocolate leads to different metabolic response to chocolate consumption. Daily intake of dark chocolate significantly increased HDL cholesterol by 6% and decreased polyunsaturated acyl ether phospholipids. Dark chocolate intake could also induce an improvement in the metabolism of long chain fatty acid, as noted by a compositional change in plasma fatty acyl carnitines. Moreover, a relationship between regular long-term dietary exposure to a small amount of dark chocolate, gut microbiota, and phenolics was highlighted, providing novel insights into biological processes associated with cocoa bioactives.

The identification of metabolites in biochemical studies is a major bottleneck in the proliferating field of metabolomics. In particular in plant metabolomics, given the diversity and abundance of endogenous secondary metabolites in plants, the identification of these is not only challenging but also essential to understanding their biological role in the plant, and their value to quality and nutritional attributes as food crops. With the new generation of analytical technologies, in which liquid chromatography (LC)-mass spectrometry (MS) and nuclear magnetic resonance (NMR) play a pioneering role, profiling metabolites in complex extracts is feasible at high throughput. However, the identification of key metabolites remains a limitation given the analytical effort necessary for traditional structural elucidation strategies. The hyphenation of LC-solid phase extraction (SPE)-NMR is a powerful analytical platform for isolating and concentrating metabolites for unequivocal identification by NMR measurements. The combination with LC-MS is a relatively straightforward approach to obtaining all necessary information for structural elucidation. Using this set-up, we could, as an example, readily identify five related glycosylated phenolic acids present in broccoli (Brassica oleracea, group Italica, cv Monaco): 1,2-di-O-E-sinapoyl-ß-gentiobiose, 1-O-E-sinapoyl-2-O-E-feruloyl-ß-gentiobiose, 1,2-di-O-E-feruloyl-ß-gentiobiose, 1,2,2'-tri-O-E-sinapoyl-ß-gentiobiose, and 1,2'-di-O-E-sinapoyl-2-O-E-feruloyl-ß-gentiobiose.

Five glucosyltransferases were cloned by RT-PCR amplification using total RNA from Hieracium pilosella L. (Asteraceae) inflorescences as template. Expression was accomplished in Escherichia coli, and three of the HIS-tagged enzymes, UGT90A7, UGT95A1, and UGT72B11 were partially purified and functionally characterised as UDP-glucose:flavonoid O-glucosyltransferases. Both UGT90A7 and UGT95A1 preferred luteolin as substrate, but possessed different regiospecificity profiles. UGT95A1 established a new subgroup within the UGT family showing high regiospecificity towards the C-3' hydroxyl group of luteolin, while UGT90A7 primarily yielded the 4'-O-glucoside, but concomitantly catalysed also the formation of the 7-O-glucoside, which could account for this flavones glucoside in H. pilosella flower heads. Semi quantitative expression profiles revealed that UGT95A1 was expressed at all stages of inflorescence development as well as in leaf and stem tissue, whereas UGT90A7 transcript abundance was nearly limited to flower tissue and started to develop with the pigmentation of closed buds. Other than these enzymes, UGT72B11 showed rather broad substrate acceptance, with highest activity towards flavones and flavonols which have not been reported from H. pilosella. As umbelliferone was also readily accepted, this enzyme could be involved in the glucosylation of coumarins and other metabolites.

Identification and quantification of metabolites occurring within specific cell types or single cells of plants and other organisms is of particular interest for natural product chemistry, chemical ecology, and biochemistry in general. The integration of studies at the gene, transcript, protein and metabolite levels in localized regions will provide useful information for the understanding of biology as a system. In this review, we summarize the latest developments in the analysis of metabolites present in small samples, micrometabolomics, dealing with sample preparation methods, with focus on laser-assisted microdissection, and the analytical technologies used. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are among the most emergent technologies in metabolomics, enabling the shortest route toward metabolite identification.

Fruit maturation and tissue differentiation are important topics in plant physiology. These biological phenomena are accompanied by specific alterations in the biological system, such as differences in the type and concentration of metabolites. The secondary metabolism of tomato (Solanum lycopersicum) fruit was monitored by using liquid chromatography (LC) coupled to photo-diode array (PDA) detection, fluorescence detection (FD), and mass spectrometry (MS). Through this integrated approach different classes of compounds were analysed: carotenoids, xanthophylls, chlorophylls, tocopherols, ascorbic acid, flavonoids, phenolic acids, glycoalkaloids, saponins, and other glycosylated derivatives. Related metabolite profiles of peel and flesh were found between several commercial tomato cultivars indicating similar metabolite trends despite the genetic background. For a single tomato cultivar, metabolite profiles of different fruit tissues (vascular attachment region, columella and placenta, epidermis, pericarp, and jelly parenchyma) were examined at the green, breaker, turning, pink, and red stages of fruit development. Unrelated to the chemical nature of the metabolites, behavioural patterns could be assigned to specific ripening stages or tissues. These findings suggest spatio-temporal specificity in the accumulation of endogenous metabolites from tomato fruit.

Untargeted metabolomics aims to gather information on as many metabolites as possible in biological systems by taking into account all information present in the data sets. Here we describe a detailed protocol for large-scale untargeted metabolomics of plant tissues, based on reversed phase liquid chromatography coupled to high-resolution mass spectrometry (LC-QTOF MS) of aqueous methanol extracts. Dedicated software, MetAlign, is used for automated baseline correction and alignment of all extracted mass peaks across all samples, producing detailed information on the relative abundance of thousands of mass signals representing hundreds of metabolites. Subsequent statistics and bioinformatics tools can be used to provide a detailed view on the differences and similarities between (groups of) samples or to link metabolomics data to other systems biology information, genetic markers and/or specific quality parameters. The complete procedure from metabolite extraction to assembly of a data matrix with aligned mass signal intensities takes about 6 days for 50 samples.

For the description of the metabolome of an organism, the development of common metabolite databases is of utmost importance. Here we present the Metabolome Tomato Database (MoTo DB), a metabolite database dedicated to liquid chromatography-mass spectrometry (LC-MS)- based metabolomics of tomato fruit (Solanum lycopersicum). A reproducible analytical approach consisting of reversed-phase LC coupled to quadrupole time-of-flight MS and photodiode array detection (PDA) was developed for large-scale detection and identification of mainly semipolar metabolites in plants and for the incorporation of the tomato fruit metabolite data into the MoTo DB. Chromatograms were processed using software tools for mass signal extraction and alignment, and intensity-dependent accurate mass calculation. The detected masses were assigned by matching their accurate mass signals with tomato compounds reported in literature and complemented, as much as possible, by PDA and MS/MS information, as well as by using reference compounds. Several novel compounds not previously reported for tomato fruit were identified in this manner and added to the database. The MoTo DB is available at http://appliedbioinformatics.wur.nl and contains all information so far assembled using this LC-PDA-quadrupole time-of-flight MS platform, including retention times, calculated accurate masses, PDA spectra, MS/MS fragments, and literature references. Unbiased metabolic profiling and comparison of peel and flesh tissues from tomato fruits validated the applicability of the MoTo DB, revealing that all flavonoids and α-tomatine were specifically present in the peel, while several other alkaloids and some particular phenylpropanoids were mainly present in the flesh tissue.

Overall metabolic modifications between fruit of light-hyperresponsive high-pigment (hp) tomato (Lycopersicon esculentum) mutant plants and isogenic nonmutant (wt) control plants were compared. Targeted metabolite analyses, as well as large-scale nontargeted mass spectrometry (MS)-based metabolite profiling, were used to phenotype the differences in fruit metabolite composition. Targeted high-performance liquid chromatography with photodiode array detection (HPLC-PDA) metabolite analyses showed higher levels of isoprenoids and phenolic compounds in hp-2(dg) fruit. Nontargeted GC-MS profiling of red fruits produced 25 volatile compounds that showed a 1.5-fold difference between the genotypes. Analyses of red fruits using HPLC coupled to high-resolution quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) in both ESI-positive and ESI-negative mode generated, respectively, 6168 and 5401 mass signals, of which 142 and 303 showed a twofold difference between the genotypes. hp-2(dg) fruits are characterized by overproduction of many metabolites, several of which are known for their antioxidant or photoprotective activities. These metabolites may now be more closely implicated as resources recruited by plants to respond to and manage light stress. The similarity in metabolic alterations in fruits of hp-1 and hp-2 mutant plants helps us to understand how hp mutations affect cellular processes.

International audience; In any metabolomics experiment, robustness and reproducibility of data collection is of vital importance. These become more important in collaborative studies where data is to be collected on multiple instruments. With minimisation of variance in sample preparation and instrument performance it is possible to elucidate even subtle differences in metabolite fingerprints due to genotype or biological treatment. In this paper we report on an inter laboratory comparison of plant derived samples by [1H]-NMR spectroscopy across five different sites and within those sites utilising instruments with different probes and magnetic field strengths of 9.4 T (400 MHz), 11.7 T (500 MHz) and 14.1 T (600 MHz). Whilst the focus of the study is on consistent data collection across laboratories, aspects of sample stability and the requirement for sample rotation within the NMR magnet are also discussed. Comparability of the datasets from participating laboratories was exceptionally good and the data were amenable to comparative analysis by multivariate statistics. Field strength differences can be adjusted for in the data pre-processing and multivariate analysis demonstrating that [1H]-NMR fingerprinting is the ideal technique for large scale plant metabolomics data collection requiring the participation of multiple laboratories.

As one of the main phytoalexins and phytoestrogens, glyceollin is an important prenylflavonoid in Glycine max [L.] Merri. (soybean). Many kinds of elicitors can be used to induce its accumulation. Its biosynthesis pathway is commonly used to study the characteristics of prenyltransferase, which catalyzes the prenylated reaction happening in a very few plant families in nature. Glycinol, the direct precursor of glyceollin, is necessary to study the prenylated reaction in soybean. In comparing with the other elicitors to elicit the glycinol accumulation in soybean cotyledons, AgNO3 is the most effective elicitor. Exposure of 6-8 days old cotyledons to 0.01 mol/L AgNO3 and incubation for 24 h result in the accumulation of 256 microg (glycinol)/g (fresh weight). The glycinol was extracted by methanol. Then the isolation and purification were conducted by preparative high performance liquid chromatography. Instead of 100% acetonitrile-0.1% formic acid as the elution system, the extract was eluted by 100% methanol-0.1% formic acid. Glycinol eluted earlier than daidzin under this system and decreased the disturbance from the large amount of daidzin. Identification was performed by comparing the mass spectrum (liquid chromatography/quadrupole-time of flight) and ultraviolet spectrum with those of the standard. At last, 100 mg purified glycinol was obtained from 390 g of fresh material

Summary Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines. Availability and implementation The AlpsNMR R package and tutorial is freely available to download from http://github.com/sipss/AlpsNMR under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.