Velitchka V. Mihaleva

Structural annotation and elucidation of conjugated phenolic compounds in black, green, and white tea extracts.

Advanced analytical approaches consisting of both LC-LTQ-Orbitrap Fourier transformed (FT)-MS and LC-time-of-flight-(TOF)-MS coupled to solid-phase extraction (SPE) NMR were used to obtain more insight into the complex phenolic composition of tea. On the basis of the combined structural information from (i) accurate mass fragmentation spectra, derived by using LC-Orbitrap FTMS(n), and (ii) proton NMR spectra, derived after LC-TOFMS triggered SPE trapping of selected compounds, 177 phenolic compounds were annotated. Most of these phenolics were glycosylated and acetylated derivatives of flavan-3-ols and flavonols. Principal component analysis based on the relative abundance of the annotated phenolic compounds in 17 commercially available black, green, and white tea products separated the black teas from the green and white teas, with epicatechin-3,5-di-O-gallate and prodelphinidin-O-gallate being among the main discriminators. The results indicate that the combined use of LC-LTQ-Orbitrap FTMS and LC-TOFMS-SPE-NMR leads to a more comprehensive metabolite description and comparison of tea and other plant samples.

A strategy for fast structural elucidation of metabolites in small volume plant extracts using automated MS-guided LC-MS-SPE-NMR.

In many metabolomics studies, metabolite identification by mass spectrometry (MS) often is hampered by the lack of good reference compounds, and hence, NMR information is essential for structural elucidation, especially for the very large group of secondary metabolites. The classical approach for compound identification is to perform time‐consuming and laborious HPLC fractionations and purifications, before (re)dissolving the molecules in deuterated solvents for NMR measurements. Hence, a more direct and easy purification protocol would save time and efforts. Here, we propose an automated MS‐guided HPLC‐MS‐solid phase extraction‐NMR approach, which was used to fully characterize flavonoid structures present in crude tomato plant extracts. NMR spectra of plant metabolites, automatically trapped and purified from LC‐MS traces, were successfully obtained, leading to the structural elucidation of the metabolites. The MS‐based trapping enabled a direct link between the mass signals and NMR peaks derived from the selected LC‐MS peaks, thereby decreasing the time needed for elucidation of the metabolite structures. In addition, automated 1H NMR spectrum fitting further speeded up the candidate rejection process. Our approach facilitates the more rapid unraveling of yet unknown metabolite structures and can therefore make untargeted metabolomics approaches more powerful. Copyright

Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index

MOTIVATION Matching both the retention index (RI) and the mass spectrum of an unknown compound against a mass spectral reference library provides strong evidence for a correct identification of that compound. Data on retention indices are, however, available for only a small fraction of the compounds in such libraries. We propose a quantitative structure-RI model that enables the ranking and filtering of putative identifications of compounds for which the predicted RI falls outside a predefined window. RESULTS We constructed multiple linear regression and support vector regression (SVR) models using a set of descriptors obtained with a genetic algorithm as variable selection method. The SVR model is a significant improvement over previous models built for structurally diverse compounds as it covers a large range (360-4100) of RI values and gives better prediction of isomer compounds. The hit list reduction varied from 41% to 60% and depended on the size of the original hit list. Large hit lists were reduced to a greater extend compared with small hit lists. AVAILABILITY http://appliedbioinformatics.wur.nl/GC-MS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A systematic approach to obtain validated Partial Least Square models for predicting lipoprotein subclasses from serum NMR spectra

A systematic approach is described for building validated PLS models that predict cholesterol and triglyceride concentrations in lipoprotein subclasses in fasting serum from a normolipidemic, healthy population. The PLS models were built on diffusion-edited (1)H NMR spectra and calibrated on HPLC-derived lipoprotein subclasses. The PLS models were validated using an independent test set. In addition to total VLDL, LDL, and HDL lipoproteins, statistically significant PLS models were obtained for 13 subclasses, including 5 VLDLs (particle size 64-31.3 nm), 4 LDLs (particle size 28.6-20.7 nm) and 4 HDLs (particle size 13.5-9.8 nm). The best models were obtained for triglycerides in VLDL (0.82 < Q(2) <0.92) and HDL (0.69 < Q(2) <0.79) subclasses and for cholesterol in HDL subclasses (0.68 < Q(2) <0.96). Larger variations in the model performance were observed for triglycerides in LDL subclasses and cholesterol in VLDL and LDL subclasses. The potential of the NMR-PLS model was assessed by comparing the LPD of 52 subjects before and after a 4-week treatment with dietary supplements that were hypothesized to change blood lipids. The supplements induced significant (p < 0.001) changes on multiple subclasses, all of which clearly exceeded the prediction errors.

MetIDB: A Publicly Accessible Database of Predicted and Experimental 1H NMR Spectra of Flavonoids

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.

1H-NMR Fingerprinting of Vaccinium vitis-idaea Flavonol Glycosides

INTRODUCTION The fruits of Vaccinium vitis-idaea L. are a valuable source of biologically active flavonoid derivatives. For studies focused on the purification of its quercetin glycosides (QGs) and related glycosides from plants and for the purpose of biological studies, the availability of numeric datasets from computer-assisted ¹H iterative full spin analysis (HiFSA), that is, ¹H-NMR fingerprinting, can replace and assist the repetitive and tedious two-dimensional NMR identification protocol required for both known and new compounds, respectively. OBJECTIVE To fully interpret the complex ¹H-NMR fingerprints of eight QGs obtained from the berries of V. vitis-idaea and provide complete and unambiguous signal assignments. METHODS Vaccinium vitis-idaea QGs were purified in a single run by long-bed gel permeation chromatography and identified by comparison with commercially available compounds using LC-MS combining ion-trap and time-of-flight detection and one- or two-dimensional NMR. The HiFSA analysis yielded full sets of ¹H chemical shifts and proton-proton coupling constants, allowing for field-independent spectral simulation. RESULTS Signal assignments were achieved for the reference standards and the QGs that dominated in purified fractions. However, even mixtures of two to three QGs could be fitted using the HiFSA approach. In the case of the overlapped sugar resonances, the initial fitting of the ¹H spectra of reference compounds, together with values extracted from the two-dimensional NMR data and literature data, assisted in the process. CONCLUSION The HiFSA method revealed for the first time the presence of Q-3-O-β-glucopyranoside and Q-3-O-β-glucuronopyranoside in the berries of V. vitis-idaea, and unambiguously confirmed the structures of Q-3-O-[4″-(3-hydroxy-3-methylglutaroyl)]-α-rhamnopyranoside, Q-3-O-α-rhamnopyranoside, Q-3-O-β-galactopyranoside, Q-3-O-α-arabinofuranoside, Q-3-O-β-xylopyranoside and Q-3-O-α-arabinopyranoside.

The effect of plant sterols and different low doses of omega-3 fatty acids from fish oil on lipoprotein subclasses

SCOPE Consumption of a low-fat spread enriched with plant sterols (PS) and different low doses (<2 g/day) of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) from fish oil reduces serum triglycerides (TGs) and low-density lipoprotein-cholesterol (LDL-Chol) and thus beneficially affects two blood lipid risk factors. Yet, their combined effects on TG and Chol in various lipoprotein subclasses have been investigated to a limited extent. METHODS AND RESULTS In a randomized, double-blind, placebo-controlled, parallel study, we determined TG and Chol in 13 LP subclasses in fasting serum of 282 hypercholesterolemic subjects, who consumed either a placebo spread or one of the four spreads containing PS (2.5 g/day) and EPA+DHA (0.0, 0.9, 1.3, and 1.8 g/day) for 4 weeks. After PS treatment, total LDL-Chol was reduced, which was not further changed by EPA+DHA. No shift in the LDL-Chol particle distribution was observed. The addition of EPA+DHA to PS dose-dependently reduced VLDL-Chol and VLDL-TG mainly in larger particles. Furthermore, the two highest doses of EPA+DHA increased Chol and TG in the larger HDL particles, while these concentrations were decreased in the smallest HDL particles. CONCLUSION The consumption of a low-fat spread enriched with both PS and EPA+DHA induced shifts in the lipoprotein distribution that may provide additional cardiovascular benefits over PS consumption alone.

Evaluation of Approaches for Quantitative Targeted Profiling of Complex Compositions using 1D 1H NMR Spectroscopy

NMR-based profiling of low molecular weight (LMW) compounds provides an ‘unbiased’ and broad view on the composition of foods and biofluids and has been proven beneficial to control the quality and safety of foods as well as to assess the effect of foods/nutrients in biological systems. The quantification of LMW compounds from 1D 1H NMR profiles has recently gained in importance for the analysis of complex mixtures, yet still suffers from major hurdles such as signal overlap preventing the accurate integration of NMR signals. Spectral fitting of overlaid signals in 1D 1H NMR profiles is a promising approach and relies on fitting procedures based on certain constraints, prior knowledge, iterative procedures and convergence criteria. In this workshop report, the most relevant features of two commercially available software packages, namely Chenomx NMR Suite and PERCH NMR software, are discussed. Both softwares are powerful programs to accurately and reproducibly quantify compounds from complex mixtures, because they are able to fit and deconvolute complex lineshapes. The user-friendliness and the comprehensive (quantitative) library of metabolites make Chenomx NMR Suite attractive, especially for non-NMR experts. In comparison, the total lineshape fitting in PERCH NMR Software is more sophisticated and can be applied in automation. In conclusion, Chenomx NMR Suite is particularly useful for small data sets comprising of mixtures with large variations in composition and concentrations, while PERCH NMR Software is the preferred program for large data sets with moderate variations.

The Large Scale Identification and Quantification of Conjugates of Intact and Gut Microbial Bioconversion Products of Polyphenols

A human diet containing a significant amount of flavonoids, such as present in tea, red wine, apple, and cocoa has been associated with reduced disease risks. After consumption, a part of these flavonoids can be directly absorbed by the small intestine, but the greatest part passages towards the large intestine where microbes break the flavonoids down into phenolic metabolites. After absorption into the blood, both intact and metabolized flavonoids are subsequently methylated, sulphated, and glucuronidated or a combination thereof. The exact chemical structural elucidation and quantification of these conjugates present in the human body are key to identify potential bioactive components. However, this is still a tedious task due to their relative low abundance in a complex background of other high-abundant metabolites and the many possible isomeric forms. Therefore, we aimed to systematically identify these conjugates by using a combination of pre-concentration and separation by solid phase extraction (SPE) followed by LC-FTMSn and 1D 1H NMR. The combination of LC-FTMSn and HPLC-TOF-MS-SPE-NMR resulted in the efficient identification and quantification of low abundant polyphenol metabolites down to micromolar concentrations and thus opens up new perspectives for in depth studying of the bioavailability and the possible mode of action of flavonoids like flavan-3-ols and their gut-microbial break-down products circulating in the human body.

Effect of Theobromine Consumption on Serum Lipoprotein Profiles in Apparently Healthy Humans with Low HDL-Cholesterol Concentrations

Scope: Theobromine is a major active compound in cocoa with allegedly beneficial effect on high-density-lipoprotein-cholesterol (HDL-CH). We have investigated the effect of theobromine (TB) consumption on the concentrations of triglyceride (TG) and cholesterol (CH) in various lipoprotein (LP) subclasses. Methods: In a randomized, double-blind, placebo-controlled, cross-over study, 44 apparently healthy women and men (age: 60 ± 6 years, BMI: 29 ± 3 kg/m2) with low baseline HDL-CH concentrations consumed a drink supplemented with 500 mg/d theobromine for 4 weeks. TG and CH concentrations in 15 LP subclasses were predicted from diffusion-edited 1H NMR spectra of fasting serum. Results: The LP phenotype of the subjects was characterized by low CH concentrations in the large HDL particles and high TG concentrations in large VLDL and chylomicron (CM) particles, which clearly differed from a LP phenotype of subjects with normal HDL-CH. TB only reduced CH concentrations in the LDL particles by 3.64 and 6.79%, but had no effect on TG and CH in any of the HDL, VLDL and CM subclasses. Conclusion: TB was not effective on HDL-CH in subjects with a LP phenotype characterized by low HDL-CH and high TG in VLDL.