Comparison of Metabolomics Approaches for Evaluating the Variability of Complex Botanical Preparations: Green Tea (Camellia sinensis) as a Case Study
- Joshua J. Kellogg
- ,
- Tyler N. Graf
- ,
- Mary F. Paine
- ,
- Jeannine S. McCune
- ,
- Olav M. Kvalheim
- ,
- Nicholas H. Oberlies
- , and
- Nadja B. Cech
View Author Information
† Department of Chemistry & Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina 27412, United States
‡ College of Pharmacy, Washington State University, Spokane, Washington 99202, United States
§ Department of Pharmaceutics, University of Washington, Seattle, Washington 99202, United States
⊥ Department of Chemistry, University of Bergen, Bergen 5020, Norway
*Tel (N. B. Cech): 336-334-3017. Fax: 336-334-5402. E-mail: [email protected]
Copyright © 2017 The American Chemical Society and American Society of Pharmacognosy. This publication is licensed under these Terms of Use.
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Abstract
A challenge that must be addressed when conducting studies with complex natural products is how to evaluate their complexity and variability. Traditional methods of quantifying a single or a small range of metabolites may not capture the full chemical complexity of multiple samples. Different metabolomics approaches were evaluated to discern how they facilitated comparison of the chemical composition of commercial green tea [Camellia sinensis (L.) Kuntze] products, with the goal of capturing the variability of commercially used products and selecting representative products for in vitro or clinical evaluation. Three metabolomic-related methods—untargeted ultraperformance liquid chromatography–mass spectrometry (UPLC-MS), targeted UPLC-MS, and untargeted, quantitative 1HNMR—were employed to characterize 34 commercially available green tea samples. Of these methods, untargeted UPLC-MS was most effective at discriminating between green tea, green tea supplement, and non-green-tea products. A method using reproduced correlation coefficients calculated from principal component analysis models was developed to quantitatively compare differences among samples. The obtained results demonstrated the utility of metabolomics employing UPLC-MS data for evaluating similarities and differences between complex botanical products.
It is common practice in many research fields to conduct in vitro or clinical evaluation of complex botanical products. The selection of appropriate study material for such investigations is confounded by the complexity and variability of botanical source material. Botanical products contain diverse phytochemicals, of which the identities of many are often not known. In addition, substantive variability in phytochemical composition exists in these products depending on the method of preparation or source material used, and industrial processing of botanical supplements frequently renders them unable to be analyzed using genetic techniques, such as DNA barcoding. (1, 2) Such variability in phytochemical composition can greatly impact the interpretation of both in vitro and clinical studies. There is currently a lack of definitive guidelines for ensuring the quality of the product to be tested. (3) The United States Food and Drug Administration (FDA) guidance for clinical trials involving botanical drug products (4) recommends that investigational new drug applications contain “a chemical identification for the active constituents or characteristic markers in the drug substance, if possible”. However, specific guidelines for comparing available products and selecting appropriate representative samples for investigation are currently lacking.
The goal of this study was to compare the effectiveness of several metabolomics approaches for evaluating the variability in the phytochemical composition of a series of commercial botanical products. Green tea [leaves from Camellia sinensis (L.) Kuntze (Theaceae)] were employed as a test case. Green tea is one of the most commonly consumed beverages worldwide (5) and is also a popular dietary supplement, ranking fifth in sales in the United States in 2015. (6) Green tea products have been reported to possess numerous health-protective qualities, including cardioprotection, chemoprevention, and weight loss. (7-9) However, many green tea clinical samples are delivered as a complex mixture (tea or extract) as opposed to single-molecule interventions. (10-12)
The phytochemical composition of green tea is similar to that of fresh Camellia sinensis leaves except for a few enzymatically catalyzed reactions that occur immediately after harvest. (5, 13) Green tea contains over 200 previously identified constituents, including polyphenols, xanthines, theanine, inorganic salts, and individual elements. (14) Polyphenols constitute up to 30% of the dry leaf by mass and are the major constituents in green tea. (15) Catechins, specifically flavan-3-ols and flavan-3-gallates, represent the largest group of polyphenols in green tea leaves and are thought to be largely responsible for the diverse bioactivity demonstrated in green tea studies. (16) The extraction efficiency of green tea polyphenols depends on the extraction method, contact time with the solvent, solvent composition, and the form of tea (i.e., bagged or loose). (17, 18) This variability is increased with the incomplete or inconsistent application of analytical methods, making determination of dose content challenging. (19) Meta-analysis studies of green tea products used in clinical studies reported polyphenol doses ranging from 200 to 1207 mg. (10-12)
Metabolomics-based approaches have emerged as important tools in assessing large chemical and biological data sets, including those related to disease pathology, (20) drug response, (21) environmental toxicity, (22) and natural products discovery. (23, 24) The primary goal of metabolomics is to correlate changes in the chemical profile of a sample with a corresponding shift in macroscopic phenotype due to a perturbation. (25) Metabolomic studies coupled with statistical analysis (chemometric studies) have been employed to characterize the relationships between the metabolome of green teas and corresponding genotype, origin, quality, or other biotic or abiotic attributes. (26-28)
Several different analytical techniques are used for metabolomic profiling, including infrared and Raman spectroscopy, NMR spectroscopy, and mass spectrometry (MS). (29, 30) NMR-based metabolomic techniques, when acquired under quantitative conditions (qNMR), offer an unbiased assessment of a complex sample composition, allow the simultaneous identification and quantification of diverse metabolites, and are nondestructive of the sample. (24) Mass spectrometry-based metabolomic methods have the advantages of orders of magnitude greater sensitivity than NMR spectroscopy and the ability to couple directly to separation techniques such as gas chromatography (GC) or liquid chromatography (LC). (31) A disadvantage of analysis via mass spectrometry is that ionization is required to detect sample components, yet all chemical compounds are not universally ionized in a mass spectrometer. (32) With these advantages and disadvantages in mind, this study was undertaken to compare the effectiveness of untargeted and targeted mass spectrometry and NMR spectroscopy as methods for chemically characterizing green tea products.
One of the critical questions in selecting a botanical product (in this case a sample of green tea) for further study is how it compares to other available products. Chemometric analysis of metabolomics data sets can be used to make these comparisons. Ascribing similarity between metabolomic profiles is often achieved via multivariate statistical modeling procedures, such as principal component analysis (PCA). (26) PCA is a graphical representation of data that can be used to ascribe clusters of similar samples, but is not equipped to quantify variability and similarity between samples, and generally employs only two principal components at a time to classify the samples. (33) Hierarchical cluster analysis (HCA) can be used to cluster samples based upon similarity, but provides only information on similarity between adjacent samples and not for overarching comparisons between all samples in a data set. (34, 35) For the work described herein, an alternate approach for comparison of samples was employed, that of a reproduced correlation coefficient matrix. The reproduced correlation coefficient matrix is based on PCA scores and loadings, but is derived from all principal components (i.e., not just a pair of components, as in traditional PCA plots). As demonstrated herein using the example of green tea, the correlation matrix displays a series of correlation coefficients that can be used to quantitatively compare multiple samples in a data set and determine which are most chemically similar. Such information can then be used to inform product selection for later in vitro or clinical evaluation.
Results and Discussion
ARTICLE SECTIONS
Comparison of Extraction Techniques
An important first step in comparing the chemistry of complex botanical products is selecting the appropriate solvent extraction technique. Two extraction techniques were considered for this study: hot water extraction and methanol extraction. Hot water extraction replicates the traditional process of brewing tea leaves and should therefore yield results relevant to consumer use. However, methanol extraction was appealing due to its nonselective ability to extract a wide range of secondary metabolites and the ease in removing methanol solvent for extract storage and processing. (36) To aid in the decision between hot water and methanol extraction, triplicate extracts of a National Institute of Standards and Technology (NIST) green tea standard were prepared in both hot water and methanol, and their chemical composition was compared.
Overall, the two different extraction techniques—hot water and methanol—yielded similar quantities of the major polyphenolic metabolites (Figure S1, Supporting Information). The metabolite profile as determined by mass spectrometric analysis appeared similar between the two techniques. The hot water extraction sample had a higher (−)-gallocatechin content relative to the methanol extraction sample, whereas the methanol extraction sample displayed higher levels of (−)-epicatechin gallate, (−)-epigallocatechin gallate, and gallic acid relative to the water extraction sample. Methanol was selected as the extraction solvent for subsequent metabolomics analysis due to the overall similarities of the extracted quantities and the ease of preparing and handling methanolic extracts.
Differentiation of Green Tea Samples by Untargeted Mass Spectrometry Metabolomics
Commercially available green tea products (n = 34) were selected using consumer sales reports (37) and product quality reports (38, 39) (Table S1, Supporting Information). A turmeric–ginger tea served as a negative control (T23), and NIST reference standards (T26, T27, and T37) served as positive controls. For the sake of further comparison, two of the selected green teas contained additional botanical additives (T24 and T38) (Table S1, Supporting Information).
Untargeted metabolomic analysis of the green tea samples using UPLC-MS yielded 2270 marker ions (unique retention time–m/z ion pairings) for 114 objects (i.e., 38 green tea samples prepared by extraction in triplicate), which were analyzed using PCA (Figure 1A). The extraction replicates (e.g., T01-1, T01-2, and T01-3) of each green tea product were overlaid on the PCA plot (Figure 1A), indicating excellent repeatability of the extraction technique and subsequent UPLC-MS analysis. PCA using untargeted metabolomics data identified one injection, T28-1, which originally appeared to be an outlier, but had been mislabeled in the UPLC injection queue. The ability to identify and address this mislabeled injection highlights the benefit and importance of having replicate samples (each analyzed separately) for metabolomic analyses.
Figure 1
Figure 1. Principal component analysis (PCA) scores plot of green tea samples drawn with Hotelling’s 95% confidence ellipse. Data points representing triplicate green tea samples were closely clustered, and distinct clusters were observed between green tea supplements, green teas, and the negative control (turmeric–ginger tea, T23, indicated in the figure as “non-green tea”). Representative samples are highlighted (T23, T24, T26, T27, and T37) to demonstrate the reproducibility of the extraction and analytical protocol. SRM represents standard reference material from the National Institute of Standards and Technology (NIST); data points indicated as “suppl” are green tea supplements.
Inspection of the data indicated that the sample clusters were located at different points in the two-dimensional space prescribed by two vectors, principal component 1 (PC1 = 38%) and principal component 2 (PC2 = 22%) (Figure 1A). Three distinct clusters were observed in the data, corresponding to the three different sample types (green tea, green tea supplement, and the non-green tea) studied. The negative control (T23) was located beyond the boundary of the Hotelling’s 95% confidence ellipse. The loose leaf and powdered green teas were grouped together and were separate from the green tea supplements. The two green teas that contained additional botanical components (T24 and T38), although roughly grouped with the green tea samples, were visibly drawn away from the main cluster of tea samples in the PCA. The positive controls (NIST samples T27 and T37) also clustered with their commercial counterparts (loose leaf tea and green tea supplements, respectively) and were located centrally within each grouping. Thus, the NIST standards do, indeed, appear to be representative of green teas used commercially by U.S. consumers. Using the unsupervised PCA analysis, it was not possible to visually differentiate between the green tea leaf and powdered samples, which suggests that the chemistry of such samples is similar.
Smaller groupings apparent within the loose leaf and powdered tea clusters were noted (Figure 1). These clusterings could have represented variations in tea cultivar, product source, and/or processing methods. (26-28, 31, 33, 40, 41) However, commercial suppliers do not traditionally offer records of cultivars, geographic locations, or precise processing procedures of their tea products. Therefore, it was not possible to determine the underlying characteristics that produced these smaller clusters. This did not present a problem for the investigation being conducted here, as the goal was not to compare green tea composition from different geographical locations but evaluate the variability in samples used by consumers and to determine which samples are most representative for further clinical and in vitro evaluation.
Averaging the peak area from each marker ion across the analyses of the triplicate extraction samples resulted in a data matrix (2270 marker ions and 38 objects) that yielded a similar clustering pattern to that observed for the individual replicates (Figure 1). The averaged data set was used for comparison against targeted chemometric analysis and NMR-based metabolomics.
The separation observed in the PCA can be explained through some of the identified metabolites highlighted in the loading plots for PC1 (Figure 2A) and PC2 (Figure 2B). Principal component loadings estimate the degree to which each independent variable contributes to the individual principal components of a PCA model; the greater the magnitude of a particular variable’s loading to a component, the more it contributes to that principal component. Plotting each principal component’s loading demonstrates which variables (marker ions) are responsible for the clusterings and shifts observed in the PCA scores plot. The loadings plot for the first principal component (Figure 2A) highlighted the catechins (−)-epigallocatechin gallate, (−)-epigallocatechin, (−)-epicatechin gallate, (−)-epicatechin and a flavonol, rutin, as all contributing negatively to PC1. These ions were the dominant marker ions responsible for separating the green tea samples from the negative control (T23) (Figure 2A), as they were present in the green tea samples in higher concentrations than the negative control. The components detected were identified by comparing retention times, accurate masses, and MS-MS fragmentation patterns against standards. Other ions that were dominant in the green tea samples included two digallate dimers of epigallocatechin gallate, theasinensin A (m/z 913.1639) and (−)-epigallocatechin-3-O-(3-O-methyl) gallate (m/z 472.1005), which were identified tentatively by comparing their accurate masses to literature values. (42, 43) It is recognized that definitive identification of these ions is not possible without isolation and confirmation with NMR spectroscopy, but such experiments were beyond the scope of the current study, for which the primary goal was to compare metabolomics data collection and analysis approaches.
Figure 2
Figure 2. Loadings plots from untargeted MS-based PCA of green tea samples. Metabolites with more negative correlation values along the x-axis (PC1, green labels) were present in higher concentrations in the green tea samples versus the negative control (T23, turmeric–ginger tea) and were responsible for the separation observed along the horizontal axis of Figure 1. Labeled metabolites with greater positive correlation along the y-axis (PC2, brown labels) were more heavily represented in green tea supplement samples versus the loose leaf green tea samples and were the dominant metabolites underlying the differentiation of the two sample groups in the vertical axis of Figure 1. Metabolites were identified by comparison against analytical standards. In cases where standards were not present, comparisons against the literature using m/z values from high-resolution mass spectrometry are provided. Identifications based on mass without reference standards are tentative.
The second principal component discriminated between the green tea supplements and the leaf/powdered teas. The corresponding loading plot revealed several metabolites that were present in higher concentrations in the green tea supplements than the leaf and powdered teas and, thus, were dominant peaks in the positive direction. Myricetin, kaempferol, and quercetin aglycones, identified via comparison against accurate mass and fragmentation pattern of standards, were all present as leading discriminating ions (Figure 2B). In addition, based upon accurate mass measurements, the tentatively identified theaflavin 3-O-(3-O-methyl) gallate, which is formed via oxidative coupling of epicatechin and epigallocatechin 3-O-(3-O-methyl) gallate, (43) and the dimer epicatechin (4β→8)-epigallocatechin-3-O-gallate (44) were observed in the positive direction of the PC2 loading plot. The data suggest that these compounds are present at higher levels in green tea supplements compared to the loose teas. Again, follow-up studies with NMR structure elucidation would be necessary to confirm the chemical identity of the 3-O-(3-O-methyl) gallate and epicatechin-(4β→8)-epigallocatechin-3-O-gallate.
Targeted Metabolomics Analysis of Green Tea Samples
As a comparison with the untargeted metabolomics approach, targeted quantitative analysis was conducted of a series of green tea components for which commercial standards were available. These standards included catechins [(+)-catechin, (−)-epicatechin, (−)-epicatechin gallate, (−)-epigallocatechin, (−)-epigallocatechin gallate, and (−)-gallocatechin], flavonols (kaempferol, myricetin, quercetin, and rutin), phenolic acids (caffeic acid, chlorogenic acid, coumaric acid, and gallic acid), an amino acid (theanine), and a purine alkaloid (caffeine). The calibration curves for each standard were linear over a range of 0.5–200 μg/mL, with a coefficient of determination (R2) > 0.992 (Table S2, Supporting Information). The 16 standards were detected in all green tea samples (Figure 3), and the concentration of each constituent was determined (Table S3, Supporting Information). Based on the corresponding heat map of quantified standards (Figure 4), concentrations of the main catechins and caffeine ranged between 7 and 107 μg/mL extract in all the green tea samples analyzed. The negative control (T23) yielded lower concentrations of most metabolites than the green tea samples, and a number of green tea metabolites were not detected (Figure 4). The NIST green tea leaf standard (T26) yielded similar concentrations of (−)-epicatechin, (−)-epicatechin gallate, (−)-gallocatechin, and gallic acid compared to the published certificate of analysis (Table S4, Supporting Information), while NIST reported higher concentrations of (−)-epigallocatechin and (−)-epigallocatechin gallate. These differences are likely a result of interlaboratory differences in extraction procedures. (45) Metabolite concentrations in all green tea samples were within the same order of magnitude as those reported by Phenol-Explorer, a database dedicated to the aggregation of phenolic content data from dietary sources, (46) and published by others conducting quantitative analysis of green tea constituents. (18, 47)
Figure 3
Figure 3. Annotated mass spectral profile identifying green tea metabolites used in this study. (A) Positive electrospray ionization mode. (B) Negative electrospray ionization mode.
Figure 4
Figure 4. Quantification of green tea standards in tea samples. Boxes represent average concentrations of triplicate samples in μg/mg extract. #Negative control (turmeric–ginger tea); ‡NIST standard reference materials; §green teas containing other botanical additives. ext, extract.
With only the 16 quantified constituents as independent variables in a 16 × 38 matrix, targeted metabolomics chemometric analysis resulted in a less discriminating PCA scores plot (Figure 5) compared to that generated with the untargeted metabolomics analysis (Figure 1). Using the targeted metabolite PCA plot, it was possible to differentiate the green tea samples from the negative control (T23). However, the NIST positive control (T26) and several leaf tea samples (T33 and T34) were interspersed among the green tea supplement samples. Thus, it was not possible to effectively discriminate between the tea and supplement samples using only the targeted metabolite data, whereas the untargeted approach (Figure 1) yielded clear delineations between the varying types of green tea. Inclusion of more standards could have potentially improved the targeted metabolite analysis; (47) however, green tea products routinely contain more than 200 known bioactive phytochemical constituents and many more undiscovered compounds. (14) Quantifying every known phytochemical would still represent a fraction of the >2000 individual marker ions used in the untargeted metabolomics approach. Thus, untargeted chemometrics provided an inherent advantage for classifying samples, which was borne out in the observed differences between PCA score plots (Figure 1 and Figure 5) and their ability to distinguish between the various green tea sample types.
Figure 5
Figure 5. Principal component analysis of targeted mass spectrometry data, drawn with Hotelling’s 95% confidence ellipse. The chemometric matrix consisted of 15 quantified samples (targeted variables) and 38 objects (for quantification data, see Table S3, Supporting Information). Representative samples are highlighted (T23, T26, T27, and T37).
Comparison of 1H NMR Spectroscopy and Mass Spectrometry Chemometric Analyses
The performance of untargeted mass spectrometry and 1H NMR chemometric analysis of green tea extract samples were further compared. Separating the 1H NMR region of δ 0.5 to 8.0 ppm into bins of 0.05 ppm yielded 150 spectral bins (independent variables) across all samples to describe the metabolite profile (Figure S2, Supporting Information).
When compared to the PCA plot obtained from untargeted mass spectrometry data (Figure 1), the NMR spectroscopic results (Figure 6) displayed similar trends in clustering tea samples. In both score plots, separation was observed between green tea samples and the negative control (T23), although in the 1H NMR PCA plot, T23 was visually closer to the boundary of Hotelling’s confidence ellipse. However, the NMR metabolomics data displayed more overlap between green tea supplement samples and loose leaf tea and powdered tea samples (Figure 6) than was observed for the untargeted mass spectrometry data (Figure 1). The overlap was due to the dispersal of variables along PC1; the variables were not clustered as cleanly as they were in the untargeted mass spectrometry analysis (Figure 1). This suggests that the spectral bins containing overlapping information lowered discrimination between tea samples. However, a higher field instrument using a cryoprobe has improved resolution. Using such an instrument (as was achieved by Yuk et al., 2013) could provide better discrimination between green teas and improve the overall metabolomics analysis. (48)
Figure 6
Figure 6. Principal component analysis (PCA) scores plot of data from 1H NMR metabolomics analysis of green tea extracts. Representative samples are highlighted (T23, T26, T27, and T37).
Comparison of Similarity Using a Reduced Correlation Matrix
One of the potential criticisms of using unsupervised statistical methods, such as PCA, to evaluate similarity between samples is the reduction of the model to only two dimensions (i.e., PC1 vs PC2), which inherently limits the analysis. For the green tea untargeted approach, PC1 and PC2 represented 60% of the total variation in the sample (38% and 22%, respectively). This has been observed in other metabolomics studies, where the principal components used for visual discrimination represented only a fraction of the total variation present in the samples. (26, 31, 40)
To address the limitations of using only two principal components to describe the variability in the data set, a “reproduced correlation matrix” (Figure 7) was calculated, which is based on four principal components and is calculated according to eq 4. Collectively, the four components used to generate the data in Figure 7 encapsulate 84% of the variation in the metabolomics data. The reproduced correlation matrix is a simple and useful way to compare differences among samples in a complex metabolomics data set. The values in the matrix range from −1.0 to 1.0, and it is possible, by selecting the relevant correlation value in the matrix, to obtain a quantitative measure of the similarity between any two samples in the data set (Table S5, Supporting Information). For example, the data set could be used to select a commercial sample that is similar to the NIST loose leaf standard (T26). Samples T02, T13, T21, and T22, which demonstrate correlations with T26 of 0.974, 0.983, 0.990, and 0.997, respectively, might be good choices. Conversely, the “superantioxidant” botanical-containing green tea T24, which shows a correlation of −0.029, is, based on the metabolomics data, less similar to the NIST standard.
Figure 7
Figure 7. Heat map correlation matrix for green tea samples. Correlation was based upon the averaged metabolomic profile for each sample and calculated from the reproduced correlation coefficient matrix comprised of a four principal component model (Table S5, Supporting Information). Darker shades represent stronger correlation between samples. #Negative control; ‡NIST standard reference materials; §green teas with botanical additives.
This study demonstrates the utility of untargeted mass spectrometry-based metabolomics to effectively discriminate between multiple classes of green tea products. Chemometric analysis using an untargeted metabolomics profiling was more effective in clustering loose leaf green teas from green tea supplements compared to targeted mass spectrometry analysis or 1H NMR metabolomics.
Previous green tea studies highlight the benefits of using NMR spectroscopy to study metabolomic differences, given that this method can detect all 1H-containing species in a sample, including phytochemical compounds that could be difficult to analyze via mass spectrometry. (27, 33, 49) One widely accepted limitation of MS has been the nonuniversality of natural product ionization; the utilization of both positive and negative modes in this study resulted in a wider range of metabolites detected and used in the overall metabolomic analysis. For the samples evaluated, the UPLC-MS data were more useful for distinguishing various sample types (i.e., supplement versus tea) than NMR-based metabolomics analysis. The improved performance of mass spectrometry-based metabolomics as compared to NMR-based metabolomics can be attributed to the ease with which mass spectrometry can be coupled to separation methods (liquid or gas chromatography), which provide another dimension of separation in the data. In addition, mass spectrometry is a far more sensitive technique than NMR spectroscopy, with limits of detection in the pM to nM range. (24, 31, 36) In contrast, NMR analysis is limited to the more abundant metabolites, which may or may not be the most relevant with respect to bioactivity. (50) Other studies have also demonstrated the improved ability of mass spectrometry to differentiate complex supplement products due to higher sensitivity. (51) Despite the somewhat superior data obtained here with mass spectrometric analysis as compared to NMR spectroscopy, results from the current work suggest that either NMR or MS could be effective methods to aid in selection of complex natural products or botanical products.
Analyzing similarity and variation from a range of commercial products remains a challenge when the study material is a complex natural product or botanical sample. (1) Green tea products, like other complex botanical preparations, contain a wide variety of bioactive secondary metabolites, which vary considerably depending on the cultivar used, geography, processing, and formulation. (52) The results illustrate the usefulness of untargeted metabolomics to obtain a snapshot of this variability. Information obtained by metabolomics analysis could be employed to make an informed opinion as to which products are most representative of those used by consumers or to identify outliers in a data set. Comparison of the data in Figure 1 (PCA based on metabolite profile) and Figure 5 (PCA based solely on representative marker compounds) indicates the advantage of making such decisions using metabolomics information rather than data for selected marker compounds. The marker compounds represent only a subset of the chemical diversity of the samples; thus, one might presume (incorrectly) based on the marker compound data that samples are chemically similar, when in fact important differences exist. Specific to the test case under investigation here, based on the PCA of complex metabolomics data (Figure 1), it is clear that the chemical makeup of green tea supplements is different from that of powdered or whole leaf tea samples. If one conducted the comparison among samples using exclusively marker compound data (Figure 5), these differences might have been overlooked. Such an oversight could have important ramifications for future studies, given that differences in the chemistry of tea versus supplement samples could lead to different results in in vitro or clinical studies.
The need to ascertain similarity and variability has numerous applications in natural products research, whether it is to monitor quality control of products for adulteration, (53, 54) authenticate botanical samples, (55) or select samples for further in vitro or in vivo studies. The chemometric approach described herein (untargeted mass spectrometric analysis coupled with reproduced data matrix calculation) has the potential to provide a wealth of data for comparisons of multiple, complex data sets. One of the challenges of using metabolite profiles to characterize similarities and differences among samples is handling the magnitude and complexity of the data that are generated with such analyses. The bottleneck for metabolomics experiments tends not to be in the data collection, but in meaningful data interpretation. An important contribution of this study is the application of the reproduced correlation coefficient matrix as a simple metric for measuring the similarity between multiple samples in a complex data set based on the whole metabolite profile. The reproduced correlation coefficient is a single value that incorporates multiple PCA model components and provides a useful alternative to visual inspection for comparing samples in a PCA plot.
Experimental Section
ARTICLE SECTIONS
Chemicals
Unless otherwise noted, all chemicals were of reagent or spectroscopic grade and obtained from Fisher Scientific (Waltham, MA, USA).
Green Tea Product Selection
Green tea products were selected using readily available consumer sales reports (37) and product quality reports. (38, 39) The 34 products included 21 whole-leaf teas, six powders, and seven supplements (Table S1, Supporting Information) and were each coded with a T number. A single non-green tea (turmeric–ginger tea) was included as a negative control (T23), and Camellia sinensis standard reference materials from NIST for loose leaf tea (T26), supplement (T27), and oral dosage form (T37) (nos. 3254, 3255, and 3256, respectively) served as positive controls (Table S1, Supporting Information). Two green teas that contained other botanical additives were also selected for comparison (T24 and T38). A retention sample of each product, containing several grams of material, was maintained in the lab for future reference.
Green Tea Product Extraction and Isolation
Green tea products were extracted in triplicate via a hot water or methanol extraction procedure. For the hot water procedure, 200 mg of sample and 20 mL of water were added to a 20 mL scintillation vial and heated to 90 °C. The mixture was stirred in a hot water bath for 5 min, after which the suspension was immediately filtered and cooled to room temperature. Each sample was lyophilized and stored at −80 °C until analysis. Sample extractions in methanol were performed in the same 10:1 ratio as the hot water extracts. Thus, to each 200 mg tea sample was added 20 mL of reagent-grade methanol, and the mixtures were shaken overnight at room temperature, filtered, and dried under reduced pressure. NMR and MS analyses were conducted upon these samples in triplicate.
1H NMR Analysis
NMR spectra were acquired with a JEOL ECA-400 NMR spectrometer (400 MHz, JEOL Ltd., Tokyo, Japan) equipped with a high-sensitivity JEOL Royal probe and a 24-slot autosampler. NMR chemical shift values were referenced to residual solvent signals for CD3OD (δH 3.31 ppm). To collect 1H NMR data, each sample was resuspended in CD3OD (Cambridge Isotope Laboratories, Andover, MA, USA) to a concentration of 10 mg/mL.
Mass Spectrometry Analysis
Ultraperformance (UP) LC-MS data were acquired using a Q Exactive Plus quadrupole-orbitrap mass spectrometer (Thermo Scientific, Waltham, MA, USA) with an electrospray ionization source coupled to an Acquity UPLC system (Waters, Milford, MA, USA). Before UPLC-MS analysis, each sample was resuspended in methanol to yield a concentration of 1 mg/mL. Triplicate injections of 3 μL were then performed. Samples were eluted from the column (Acquity UPLC BEH C18 1.7 μm, 2.1 × 50 mm, Waters) at a flow rate of 0.3 mL/min using the following binary gradient, with solvent A consisting of H2O (0.1% formic acid added) and solvent B consisting of CH3CN (0.1% formic acid added): initial isocratic composition of 95:5 (A:B) for 1.0 min, increasing linearly to 0:100 over 20 min, followed by an isocratic hold at 0:100 for 1 min, gradient returned to starting conditions of 95:5 for 2 min, and held isocratic again for 1 min. The mass spectrometer was operated in the positive/negative switching ionization mode over a full scan range of m/z 150–2000 with the following settings: capillary voltage, 5 V; capillary temperature, 300 °C; tube lens offset, 35 V; spray voltage, 3.80 kV; sheath gas flow and auxiliary gas flow, 35 and 20 arbitrary units, respectively.
Metabolite Quantification
Quantification of the major catechin, phenolic acid, and flavonoid components of the green tea products used 15 calibration standards obtained from Chromadex (Irvine, CA, USA) (Table S2, Supporting Information). LC-MS analysis was conducted as described above. Standards were prepared in spectrometric-grade MeOH and diluted in a 2-fold dilution series ranging from 0.1 to 200 μg/mL before injection. A calibration curve was constructed by plotting the area of the selected ion chromatogram for each standard versus nominal concentration. Concentrations of each standard in the extracts were determined by 1/x2 weighted least-squares linear regression.
Chemometric Analysis
Chemometric analysis was conducted using a slightly modified version of a previously reported method. (56) Both the untargeted and targeted UPLC-MS data sets for each sample were individually analyzed, aligned, and filtered with MZmine 2.20 software (http://mzmine.sourceforge.net/). (57) Peak detection in MZmine was achieved using the following parameters for peak detection: noise level (absolute value), 5 × 105 counts; minimum peak duration, 0.05 min; tolerance for m/z variation, 0.05; and tolerance for m/z intensity variation, 20%. Deisotoping, peak list filtering, and retention time alignment algorithm packages were used to refine peak detection. Finally, the join algorithm integrated all metabolomic profiles into a single data matrix using the following parameters: the balance between m/z and retention time was set at 10.0 each, m/z tolerance was set at 0.001, and retention time tolerance size was defined as 0.5 min. The spectral data matrix was exported for analysis, both as a set of peak areas for individual ions detected in triplicate extractions and the average peak areas for the triplicate extractions. Throughout the MZmine data processing steps, samples that did not possess a particular marker ion were coded with a peak area of 0, to maintain the same number of variables for all data sets. Chemometric analysis was performed on the data sets (both the individual triplicate data and the average of the triplicates for each sample) using Sirius version 9.0 (Pattern Recognition Systems AS, Bergen, Norway). (58) Initially, transformation from heteroscedastic to homoscedastic noise was carried out by a fourth root transform of the spectral variables. (59) Principal component analysis was used to provide unsupervised statistical analysis of the green tea samples.
Correlation coefficients (r) were calculated from principal component models and used to indicate similarity between samples. A data matrix X can be decomposed into the sum of the mean values of the variables (X̅), the data estimated from principal components representing the major variation in X (X̂PCA), and residual data (noise and other sources of small variation, EPCA) (eq 1).
(1)
(1)The product of the estimated PCA matrix and its transpose (X̂PCAT) divided by the norm of the two matrices yields a reproduced correlation coefficient matrix that can be used for comparison between variables (eq 2) or objects (samples) (eq 3):
(2)
(3)
(2)(3)Thus, the reproduced correlation coefficient between each pair of variables (eq 2) or objects (eq 3) can be determined from the scalar product divided by the norm of the vectors (X̂PCA) (eq 4). Thus, for any two objects, 
and 
,
(4)
and ,(4)Equation 4 provides a correlation coefficient that describes the extent to which a given sample (in the present case, a green tea extract) correlates with any other sample in the data set after removing noise and other sources of small variation from the data. Coefficient values closer to 1 demonstrate a stronger correlation (i.e., greater similarity) between the two samples. This calculation was performed in Excel (Microsoft, Redmond, WA, USA), using the PCA loading and score information obtained from the Sirius software output.
For NMR-based metabolomics, NMR spectra were processed using Mnova (Mestrelab Research, Santiago de Compostela, Spain) with exponential apodization (exponent 1); global phase correction; Bernstein-Polynomial baseline correction; Savitzky–Golay line smoothing; and normalization using total spectral area as provided in Mnova. Spectral regions from 0.5 to 8 ppm were included in the normalization and analysis. The NMR spectra of all the green tea samples were binned by 0.05 ppm. The narrow bin size allowed details to be revealed and provided much information on low-intensity peaks. (60) In this data set, each bin was considered to be one variable. Binned data were used to conduct PCA using the Sirius software.
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- Joshua J. Kellogg - Department of Chemistry & Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina 27412, United States;http://orcid.org/0000-0001-8685-0353
- Nicholas H. Oberlies - Department of Chemistry & Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina 27412, United States;http://orcid.org/0000-0002-0354-8464
Acknowledgment
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This project was supported by the National Institutes of Health National Center for Complementary and Integrative Health, specifically the Center of Excellence for Natural Product Drug Interaction Research (NaPDI, U54 AT008909). We thank Dr. D. C. Hopp, NIH Program Officer, and Dr. D. D. Shen, Professor of Pharmaceutics at the University of Washington, for helpful discussions. The high-resolution mass spectrometry data were collected at the Triad Mass Spectrometry Facility at the University of North Carolina at Greensboro.
References
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This article references 60 other publications.
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- 19Balentine, D. A.; Dwyer, J. T.; Erdman, J. W., Jr.; Ferruzzi, M. G.; Gaine, C.; Harnly, J. M.; Kwik-Uribe, C. L. Am. J. Clin. Nutr. 2015, 101, 1113– 1125 DOI: 10.3945/ajcn.113.071274Google Scholar19https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVOjtbbE&md5=8bc0b222b663a02228fc54dcc634ff77Recommendations on reporting requirements for flavonoids in researchBalentine, Douglas A.; Dwyer, Johanna T.; Erdman, John W., Jr.; Ferruzzi, Mario G.; Gaine, P. Courtney; Harnly, James M.; Kwik-Uribe, Catherine L.American Journal of Clinical Nutrition (2015), 101 (6), 1113-1125CODEN: AJCNAC; ISSN:0002-9165. (American Society for Nutrition)Numerous observational and intervention-based human studies support the notion of a beneficial role for dietary flavonoids in human health. Despite these studies, it is not yet possible to make dietary recommendations with regard to the types and amts. of flavonoids to be consumed. The inherent diversity of flavonoid structure, chem., and natural distribution in foods lends itself to errors in reporting the types and/or amts. of flavonoids consumed, as well as incomplete recognition of requirements for intervention studies that aim to assess their benefits in a clin. setting. A need exists for guidelines that facilitate the design and reporting of flavonoid research. With a focus on clin. studies, this article (1) outlines limitations commonly encountered in the field of flavonoid research, including the inconsistent use of nomenclature, inappropriate analytic methods, inconsistent use of existing flavonoid databases, and the lack of full consideration in the design of test materials for intervention trials, and (2) provides guidance for future studies with a focus on clin. intervention trials. Adoption of this guidance will facilitate more accurate and interpretable research that will support the development of dietary recommendations regarding the intake of flavonoids.
- 20Sreekumar, A.; Poisson, L. M.; Rajendiran, T. M.; Khan, A. P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R. J.; Li, Y.; Nyati, M. K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G. S.; Ghosh, D.; Pennathur, S.; Alexander, D. C.; Berger, A.; Shuster, J. R.; Wei, J. T.; Varambally, S.; Beecher, C.; Chinnaiyan, A. M. Nature 2009, 457, 910– 914 DOI: 10.1038/nature07762Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhslCnt70%253D&md5=8f67dd4877a80cebd671c8d5de05bcdfMetabolomic profiles delineate potential role for sarcosine in prostate cancer progressionSreekumar, Arun; Poisson, Laila M.; Rajendiran, Thekkelnaycke M.; Khan, Amjad P.; Cao, Qi; Yu, Jindan; Laxman, Bharathi; Mehra, Rohit; Lonigro, Robert J.; Li, Yong; Nyati, Mukesh K.; Ahsan, Aarif; Kalyana-Sundaram, Shanker; Han, Bo; Cao, Xuhong; Byun, Jaeman; Omenn, Gilbert S.; Ghosh, Debashis; Pennathur, Subramaniam; Alexander, Danny C.; Berger, Alvin; Shuster, Jeffrey R.; Wei, John T.; Varambally, Sooryanarayana; Beecher, Christopher; Chinnaiyan, Arul M.Nature (London, United Kingdom) (2009), 457 (7231), 910-914CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)Multiple, complex mol. events characterize cancer development and progression. Deciphering the mol. networks that distinguish organ-confined disease from metastatic disease may lead to the identification of crit. biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumors, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination of high-throughput liq.-and-gas-chromatog.-based mass spectrometry, the authors profiled more than 1126 metabolites across 262 clin. samples related to prostate cancer (42 tissues and 110 each of urine and plasma). These unbiased metabolomic profiles were able to distinguish benign prostate, clin. localized prostate cancer and metastatic disease. Sarcosine, an N-Me deriv. of the amino acid glycine, was identified as a differential metabolite that was highly increased during prostate cancer progression to metastasis and can be detected non-invasively in urine. Sarcosine levels were also increased in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-Me transferase, the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addn. of exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degrdn., sarcosine dehydrogenase, induced an invasive phenotype in benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway. Here, by profiling the metabolomic alterations of prostate cancer progression, the authors reveal sarcosine as a potentially important metabolic intermediary of cancer cell invasion and aggressivity.
- 21Clayton, T. A.; Lindon, J. C.; Cloarec, O.; Antti, H.; Charuel, C.; Hanton, G.; Provost, J. P.; Le Net, J. L.; Baker, D.; Walley, R. J.; Everett, J. R.; Nicholson, J. K. Nature 2006, 440, 1073– 1077 DOI: 10.1038/nature04648Google ScholarThere is no corresponding record for this reference.
- 22Cotton, J.; Leroux, F.; Broudin, S.; Marie, M.; Corman, B.; Tabet, J.-C.; Ducruix, C.; Junot, C. J. Agric. Food Chem. 2014, 62, 11335– 11345 DOI: 10.1021/jf504400cGoogle ScholarThere is no corresponding record for this reference.
- 23Krug, D.; Müller, R. Nat. Prod. Rep. 2014, 31, 768– 783 DOI: 10.1039/c3np70127aGoogle Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXotFCitbc%253D&md5=e19c742793e6fa01a7f784f18910cd2fSecondary metabolomics: the impact of mass spectrometry-based approaches on the discovery and characterization of microbial natural productsKrug, Daniel; Muller, RolfNatural Product Reports (2014), 31 (6), 768-783CODEN: NPRRDF; ISSN:0265-0568. (Royal Society of Chemistry)A review. The in-depth anal. of secondary metabolomes of many microbes offers tremendous opportunities for the discovery of novel natural products which often exhibit promising biol. activities. However, over the last years the increasing availability of whole-genome information has led to raised expectations, as bioinformatic anal. revealed that traditional strategies to discover novel secondary metabolites apparently have so far only scratched the surface of the real microbial "secondary metabolome landscape". Metabolomics-based approaches using modern mass spectrometry techniques can help to bridge the gap between genome-encoded potential for the prodn. of secondary metabolites and the usually contradictory low nos. of compds. known from a specific producer. In this article recent studies are highlighted in which metabolomics-driven anal. played a crucial role for the discovery of novel secondary metabolites from microbial sources. The authors also exemplify how the implementation of metabolomics techniques facilitates the structural characterization of novel metabolites and contributes to the in-depth investigation of underlying biosynthetic pathways. Furthermore, the constantly increasing role of secondary metabolomics for the identification of novel natural products in a drug discovery context is discussed.
- 24Simmler, C.; Kulakowski, D.; Lankin, D. C.; McAlpine, J. B.; Chen, S. N.; Pauli, G. F. Adv. Nutr. 2016, 7, 179– 189 DOI: 10.3945/an.115.009928Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitVOjurvE&md5=3cfc648cb35b6cadfade91de685109dcHolistic analysis enhances the description of metabolic complexity in dietary natural productsSimmler, Charlotte; Kulakowski, Daniel; Lankin, David C.; McAlpine, James B.; Chen, Shao-Nong; Pauli, Guido F.Advances in Nutrition (2016), 7 (1), 179-189CODEN: ANDUAW; ISSN:2156-5376. (American Society for Nutrition)In the field of food and nutrition, complex natural products (NPs) are typically obtained from cells/tissues of diverse organisms such as plants, mushrooms, and animals. Among them, edible fruits, grains, and vegetables represent most of the human diet. Because of an important dietary dependence, the comprehensive metabolomic anal. of dietary NPs, performed holistically via the assessment of as many metabolites as possible, constitutes a fundamental building block for understanding the human diet. Both mass spectrometry (MS) and NMR (NMR) are important complementary analytic techniques, covering a wide range of metabolites at different concns. Particularly, 1-dimensional 1H-NMR offers an unbiased overview of all metabolites present in a sample without prior knowledge of its compn., thereby leading to an untargeted anal. In the past decade, NMR-based metabolomics in plant and food analyses has evolved considerably. The scope of the present review, covering literature of the past 5 y, is to address the relevance of 1H-NMR-based metabolomics in food plant studies, including a comparison with MS-based techniques. Major applications of NMR-based metabolomics for the quality control of dietary NPs and assessment of their nutritional values are presented.
- 25Oliver, S. G.; Winson, M. K.; Kell, D. B.; Baganz, F. Trends Biotechnol. 1998, 16, 373– 378 DOI: 10.1016/S0167-7799(98)01214-1Google ScholarThere is no corresponding record for this reference.
- 26Ku, K. M.; Choi, J. N.; Kim, J.; Kim, J. K.; Yoo, L. G.; Lee, S. J.; Hong, Y.-S.; Lee, C. H. J. Agric. Food Chem. 2010, 58, 418– 426 DOI: 10.1021/jf902929hGoogle ScholarThere is no corresponding record for this reference.
- 27Lee, J.-E.; Lee, B.-J.; Chung, J.-O.; Hwang, J.-A.; Lee, S.-J.; Lee, C.-H.; Hong, Y.-S. J. Agric. Food Chem. 2010, 58, 10582– 10589 DOI: 10.1021/jf102415mGoogle ScholarThere is no corresponding record for this reference.
- 28Lee, J.-E.; Lee, B.-J.; Chung, J.-O.; Kim, H. N.; Kim, E.-H.; Jung, S.; Lee, H.; Lee, S. J.; Hong, Y.-S. Food Chem. 2015, 174, 452– 459 DOI: 10.1016/j.foodchem.2014.11.086Google ScholarThere is no corresponding record for this reference.
- 29Zhang, A.; Sun, H.; Wang, P.; Han, Y.; Wang, X. Analyst 2012, 137, 293– 300 DOI: 10.1039/C1AN15605EGoogle ScholarThere is no corresponding record for this reference.
- 30Scalbert, A.; Brennan, L.; Manach, C.; Andres-Lacueva, C.; Dragsted, L. O.; Draper, J.; Rappaport, S. M.; van der Hooft, J. J. J.; Wishart, D. S. Am. J. Clin. Nutr. 2014, 99, 1286– 1308 DOI: 10.3945/ajcn.113.076133Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXovFGmsbs%253D&md5=8179a9816055829b29bbf4e2cc254973The food metabolome: a window over dietary exposureScalbert, Augustin; Brennan, Lorraine; Manach, Claudine; Andres-Lacueva, Cristina; Dragsted, Lars O.; Draper, John; Rappaport, Stephen M.; van der Hooft, Justin J. J.; Wishart, David S.American Journal of Clinical Nutrition (2014), 99 (6), 1286-1308CODEN: AJCNAC; ISSN:0002-9165. (American Society for Nutrition)A review. The food metabolome is defined as the part of the human metabolome directly derived from the digestion and biotransformation of foods and their constituents. With >25,000 compds. known in various foods, the food metabolome is extremely complex, with a compn. varying widely according to the diet. By its very nature it represents a considerable and still largely unexploited source of novel dietary biomarkers that could be used to measure dietary exposures with a high level of detail and precision. Most dietary biomarkers currently have been identified on the basis of our knowledge of food compns. by using hypothesis-driven approaches. However, the rapid development of metabolomics resulting from the development of highly sensitive modern analytic instruments, the availability of metabolite databases, and progress in (bio)informatics has made agnostic approaches more attractive as shown by the recent identification of novel biomarkers of intakes for fruit, vegetables, beverages, meats, or complex diets. Moreover, examples also show how the scrutiny of the food metabolome can lead to the discovery of bioactive mols. and dietary factors assocd. with diseases. However, researchers still face hurdles, which slow progress and need to be resolved to bring this emerging field of research to maturity. These limits were discussed during the First International Workshop on the Food Metabolome held in Glasgow. Key recommendations made during the workshop included more coordination of efforts; development of new databases, software tools, and chem. libraries for the food metabolome; and shared repositories of metabolomic data. Once achieved, major progress can be expected toward a better understanding of the complex interactions between diet and human health.
- 31Fujimura, Y.; Kurihara, K.; Ida, M.; Kosaka, R.; Miura, D.; Wariishi, H.; Maeda-Yamamoto, M.; Nesumi, A.; Saito, T.; Kanda, T.; Yamada, K.; Tachibana, H. PLoS One 2011, 6, e23426 DOI: 10.1371/journal.pone.0023426Google ScholarThere is no corresponding record for this reference.
- 32Cech, N. B.; Yu, K. LCGC North America 2013, 31, 938– 947Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXltV2ltg%253D%253D&md5=e36f33e7094c66512117978c53cdea6fMass spectrometry for natural products research: challenges, pitfalls, and opportunitiesCech, Nadja B.; Yu, KateLCGC North America (2013), 31 (11), 938, 940-947CODEN: LNACBH; ISSN:1527-5949. (Advanstar Communications, Inc.)A review. A common attitude in natural products research is that NMR (NMR) spectroscopy serves as a primary tool, whereas mass spectrometry (MS) is relegated to the task of providing the mol. formulas of pure compds. Yet over the past several decades, we have witnessed astonishing growth in MS. Electrospray ionization has enabled the anal. of biol. mols. previously deemed intractable, and instruments that offer astounding mass accuracy are becoming routinely available. Nonetheless, as applied to natural products research, MS is still fraught with challenges and pitfalls. Here is an account of strategies to conduct effective research despite these obstacles.
- 33Lee, J.-E.; Lee, B.-J.; Hwang, J.-A.; Ko, K.-S.; Chung, J.-O.; Kim, E.-H.; Lee, S.-J.; Hong, Y.-S. J. Agric. Food Chem. 2011, 59, 10579– 10585 DOI: 10.1021/jf202304zGoogle ScholarThere is no corresponding record for this reference.
- 34Machado, F. L. S.; Duarte, H. M.; Gestinari, L. M. S.; Cassano, V.; Kaiser, C. R.; Soares, A. R. Chem. Biodiversity 2016, 13, 845– 851 DOI: 10.1002/cbdv.201500246Google ScholarThere is no corresponding record for this reference.
- 35Siheri, W.; Zhang, T.; Ebiloma, G. U.; Biddau, M.; Woods, N.; Hussain, M. Y.; Clements, C. J.; Fearnley, J.; Ebel, R. E.; Paget, T.; Muller, S.; Carter, K. C.; Ferro, V. A.; De Koning, H. P.; Watson, D. G. PLoS One 2016, 11, 1– 16 DOI: 10.1371/journal.pone.0155355Google ScholarThere is no corresponding record for this reference.
- 36Mushtaq, M. Y.; Choi, Y. H.; Verpoorte, R.; Wilson, E. G. Phytochem. Anal. 2014, 25, 291– 306 DOI: 10.1002/pca.2505Google Scholar36https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXitlSitbo%253D&md5=cad0a3ff9eee87268369460a70834a25Extraction for Metabolomics: Access to The MetabolomeMushtaq, Mian Yahya; Choi, Young Hae; Verpoorte, Robert; Wilson, Erica G.Phytochemical Analysis (2014), 25 (4), 291-306CODEN: PHANEL; ISSN:0958-0344. (John Wiley & Sons Ltd.)A review. The value of information obtained from a metabolomic study depends on how much of the metabolome is present in analyzed samples. Thus, only a comprehensive and reproducible extn. method will provide reliable data because the metabolites that will be measured are those that were extd. and all conclusions will be built around this information. The objective was to discuss the efficiency and reliability of available sample pre-treatment methods and their application in different fields of metabolomics. The review has three sections: the first deals with pre-extn. techniques, the second discusses the choice of extn. solvents and their main features and the third includes a brief description of the most used extn. techniques: microwave-assisted extn., solid-phase extn., supercrit. fluid extn., Soxhlet and a new method developed in our lab. - the comprehensive extn. method. ResultsExamn. of over 200 studies showed that sample collection, homogenisation, grinding and storage could affect the yield and reproducibility of results. They also revealed that apart from the solvent used for extn., the extn. techniques have a decisive role on the metabolites available for anal. It is essential to evaluate efficacy and reproducibility of sample pre-treatment as a first step to ensure the reliability of a metabolomic study. Among the reviewed methods, the comprehensive extn. method appears to provide a promising approach for extg. diverse types of metabolites. Copyright © 2014 John Wiley & Sons, Ltd.
- 37Amazon.com Amazon Best Sellers. http://www.amazon.com/Best-Sellers-Grocery-Gourmet-Food-Green-Tea-Beverages/zgbs/grocery/16318471 (November 11, 2015) ,.Google ScholarThere is no corresponding record for this reference.
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- 40Ku, K. M.; Kim, J.; Park, H. J.; Liu, K. H.; Lee, C. H. J. Agric. Food Chem. 2010, 58, 345– 352 DOI: 10.1021/jf902818cGoogle ScholarThere is no corresponding record for this reference.
- 41Lee, L.-S.; Choi, J. H.; Son, N.; Kim, S.-H.; Park, J.-D.; Jang, D.-J.; Jeong, Y.; Kim, H.-J. J. Agric. Food Chem. 2013, 61, 332– 338 DOI: 10.1021/jf304161yGoogle ScholarThere is no corresponding record for this reference.
- 42Sang, S.; Lee, M.-H.; Ho, C.-T.; Yang, C. S. J. Agric. Food Chem. 2005, 53, 9478– 9484 DOI: 10.1021/jf0519055Google ScholarThere is no corresponding record for this reference.
- 43Nishimura, M.; Ishiyama, K.; Watanabe, A.; Kawano, S.; Miyase, T.; Sano, M. J. Agric. Food Chem. 2007, 55, 7252– 7257 DOI: 10.1021/jf070312mGoogle ScholarThere is no corresponding record for this reference.
- 44Yokozawa, T.; Nakagawa, T. Food Chem. Toxicol. 2004, 42, 975– 981 DOI: 10.1016/j.fct.2004.02.010Google Scholar44https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXjsVWhsb0%253D&md5=159d711e8ff5acc6db90dda96ebfb781Inhibitory effects of Luobuma tea and its components against glucose-mediated protein damageYokozawa, Takako; Nakagawa, TakakoFood and Chemical Toxicology (2004), 42 (6), 975-981CODEN: FCTOD7; ISSN:0278-6915. (Elsevier Science B.V.)Luobuma tea, prepd. from the leaves of Apocynum venetum L., is a popular beverage in China. In this study, the activity of Luobuma leaf ext. and its components against the formation of advanced glycation endproducts (AGEs), which are largely involved in the pathogenesis of diabetic vascular complications, was examd. using the in vitro glycation reaction. Strong inhibitory activity against the formation of AGEs was shown by Luobuma aq. ext. Following further fractionation of this ext., seven polyphenolic compds., i.e. (±)-gallocatechin, (-)-epigallocatechin, (±)-catechin, (-)-epicatechin, epicatechin-(4β-8)-gallocatechin, epigallocatechin-(4β-8)-epicatechin and procyanidin B-2, were isolated by Sephadex LH-20 column chromatog. These purified compds. also exerted inhibitory activities that were more potent than the pos. control, aminoguanidine. Our findings may help to explain the beneficial effects of this plant against atherosclerosis.
- 45National Institute of Standards and Technology, Standard Reference Material 3254,Certificate of Analysis. In (2016.Google ScholarThere is no corresponding record for this reference.
- 46Rothwell, J. A.; Pérez-Jiménez, J.; Neveu, V.; Medina-Ramon, A.; M’Hiri, N.; Garcia Lobato, P.; Manach, C.; Knox, K.; Eisner, R.; Wishart, D.; Scalbert, A. Database 2013) 2013, bat070 DOI: 10.1093/database/bat070Google ScholarThere is no corresponding record for this reference.
- 47Zhao, Y.; Chen, P.; Lin, L.; Harnly, J. M.; Liangli, Y.; Li, Z. Food Chem. 2011, 126, 1269– 1277 DOI: 10.1016/j.foodchem.2010.11.055Google ScholarThere is no corresponding record for this reference.
- 48Yuk, J.; McIntyre, K. L.; Fischer, C.; Hicks, J.; Colson, K. L.; Lui, E.; Brown, D.; Arnason, J. T. Anal. Bioanal. Chem. 2013, 405, 4499– 4509 DOI: 10.1007/s00216-012-6582-6Google Scholar48https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhvVGqs7bE&md5=c8423d8f0a1a1a34291f68e23e9385baDistinguishing Ontario ginseng landraces and ginseng species using NMR-based metabolomicsYuk, Jimmy; McIntyre, Kristina L.; Fischer, Christian; Hicks, Joshua; Colson, Kimberly L.; Lui, Ed; Brown, Dan; Arnason, John T.Analytical and Bioanalytical Chemistry (2013), 405 (13), 4499-4509CODEN: ABCNBP; ISSN:1618-2642. (Springer)The use of 1H-NMR-based metabolomics to distinguish and identify unique markers of five Ontario ginseng (Panax quinquefolius) landraces and two ginseng species (P. quinquefolius and P. ginseng) was evaluated. Three landraces (2, 3, and 5) were distinguished from one another in the principal component anal. (PCA) scores plot. Further anal. was conducted and specific discriminating metabolites from the PCA loadings were detd. Landraces 3 and 5 were distinguishable on the basis of a decreased NMR intensity in the Me ginsenoside region, indicating decreased overall ginsenoside levels. In addn., landrace 5 was sepd. by an increased amt. of sucrose relative to the rest of the landraces. Landrace 2 was sepd. from the rest of the landraces by the increased level of ginsenoside Rb1. The Ontario P. quinquefolius was also compared with Asian P. ginseng by PCA, and clear sepn. between the two groups was detected in the PCA scores plot. The PCA loadings plot and a t-test NMR difference plot were able to identify an increased level of maltose and a decreased level of sucrose in the Asian ginseng compared with the Ontario ginseng. An overall decrease of ginsenoside content, esp. ginsenoside Rb1, was also detected in the Asian ginseng's metabolic profile. This study demonstrates the potential of NMR-based metabolomics as a powerful high-throughput technique in distinguishing various closely related ginseng landraces and its ability to identify metabolic differences from Ontario and Asian ginseng. The results from this study will allow better understanding for quality assessment, species authentication, and the potential for developing a fully automated method for quality control.
- 49Lee, J.-E.; Lee, B.-J.; Chung, J.-O.; Shin, H.-J.; Lee, S.-J.; Lee, C.-H.; Hong, Y.-S. Food Res. Int. 2011, 44, 597– 604 DOI: 10.1016/j.foodres.2010.12.004Google Scholar49https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXjs1CmtLo%253D&md5=4eda441cad6318cf8fed6b7d253b10f11H NMR-based metabolomic characterization during green tea (Camellia sinensis) fermentationLee, Jang-Eun; Lee, Bum-Jin; Chung, Jin-Oh; Shin, Hyun-Jung; Lee, Sang-Jun; Lee, Cherl-Ho; Hong, Young-ShickFood Research International (2011), 44 (2), 597-604CODEN: FORIEU; ISSN:0963-9969. (Elsevier B.V.)The metabolic behavior of green tea (Camellia sinensis) during tea fermn. was characterized by 1H NMR spectroscopy coupled with multivariate statistical anal. to provide comprehensive information on changes in metabolites induced by tea fermn. Fourteen tea metabolites of epicatechin (EC), epigallocatechin (EGC), epicatechin-3-gallate (ECG), epigallocatechin-3-gallate (EGCG), theanine, alanine, acetate, quinate, glutamate, caffeine, sucrose, glucose, and gallate, as identified by 1H NMR spectroscopy, were responsible for metabolic differentiation between green tea and fermented tea by principal component anal. During tea fermn., levels of EC, EGC, ECG, EGCG, quinate, caffeine, and sucrose were decreased, whereas gallate and glucose levels were increased. In particular, unique changes in caffeine and gallate levels were obsd. during tea fermn., which caffeine and gallate levels have been shown to vary after tea fermn. among many reports to date. This study highlights that metabolomics with global profiling and a highly reliable and reproducible 1H NMR spectroscopic data set can provide a better understanding of unique changes in tea metabolites during tea fermn.
- 50Farag, M. A.; Porzel, A.; Al-Hammady, M. A.; Hegazy, M.-E. F.; Meyer, A.; Mohamed, T. A.; Westphal, H.; Wessjohann, L. A. J. Proteome Res. 2016, 15, 1274– 1287 DOI: 10.1021/acs.jproteome.6b00002Google Scholar50https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xislait7Y%253D&md5=af43b6fb212490b53f7521ebf1033297Soft Corals Biodiversity in the Egyptian Red Sea: A Comparative MS and NMR Metabolomics Approach of Wild and Aquarium Grown SpeciesFarag, Mohamed A.; Porzel, Andrea; Al-Hammady, Montasser A.; Hegazy, Mohamed-Elamir F.; Meyer, Achim; Mohamed, Tarik A.; Westphal, Hildegard; Wessjohann, Ludger A.Journal of Proteome Research (2016), 15 (4), 1274-1287CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)Marine life has developed unique metabolic and physiol. capabilities and advanced symbiotic relationships to survive in the varied and complex marine ecosystems. Herein, metabolite compn. of the soft coral genus Sarcophyton was profiled with respect to its species and different habitats along the coastal Egyptian Red Sea via 1H NMR and ultra performance liq. chromatog.-mass spectrometry (UPLC-MS) large-scale metabolomics analyses. The current study extends the application of comparative secondary metabolite profiling from plants to corals revealing for metabolite compositional differences among its species via a comparative MS and NMR approach. This was applied for the first time to investigate the metab. of 16 Sarcophyton species in the context of their genetic diversity or growth habitat. Under optimized conditions, we were able to simultaneously identify 120 metabolites including 65 diterpenes, 8 sesquiterpenes, 18 sterols, and 15 oxylipids. Principal component anal. (PCA) and orthogonal projection to latent structures-discriminant anal. (OPLS) were used to define both similarities and differences among samples. For a compd. based classification of coral species, UPLC-MS was found to be more effective than NMR. The main differentiations emanate from cembranoids and oxylipids. The specific metabolites that contribute to discrimination between soft corals of S. ehrenbergi from the three different growing habitats also belonged to cembrane type diterpenes, with aquarium S. ehrenbergi corals being less enriched in cembranoids compared to sea corals. PCA using either NMR or UPLC-MS data sets was found equally effective in predicting the species origin of unknown Sarcophyton. Cyclopropane contg. sterols obsd. in abundance in corals may act as cellular membrane protectant against the action of coral toxins, i.e., cembranoids.
- 51Farag, M. A.; Porzel, A.; Wessjohann, L. A. Phytochemistry 2012, 76, 60– 72 DOI: 10.1016/j.phytochem.2011.12.010Google ScholarThere is no corresponding record for this reference.
- 52Saldanha, L.; Dwyer, J. T.; Andrews, K.; Betz, J.; Harnly, J.; Pehrsson, P.; Rimmer, C.; Savarala, S. J. Food Sci. 2015, 80, H883– H888 DOI: 10.1111/1750-3841.12838Google ScholarThere is no corresponding record for this reference.
- 53Bajpai, V.; Singh, A.; Arya, K. R.; Srivastava, M.; Kumar, B. Food Addit. Contam., Part A 2015, 32, 799– 807 DOI: 10.1080/19440049.2015.1022885Google ScholarThere is no corresponding record for this reference.
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- 55Yuk, J.; Patel, D. N.; Isaac, G.; Smith, K.; Wrona, M.; Olivos, H. J.; Yu, K. J. Braz. Chem. Soc. 2016, 27, 1476– 1483 DOI: 10.5935/0103-5053.20160189Google Scholar55https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xhsl2nt73N&md5=d43d3750a5ca951c07ee7e73a11f0970Chemical profiling of ginseng species and ginseng herbal products using UPLC/QTOF-MSYuk, Jimmy; Patel, Dhavalkumar N.; Isaac, Giorgis; Smith, Kerri; Wrona, Mark; Olivos, Hernando J.; Yu, KateJournal of the Brazilian Chemical Society (2016), 27 (8), 1476-1483CODEN: JOCSET; ISSN:0103-5053. (Sociedade Brasileira de Quimica)The chem. profiles of four ginseng roots samples from three species of ginseng (Panax quinquefolius, Panax ginseng and Panax notoginseng) and two com. ginseng products contg. P. quinquefolius and red P. ginseng were compared using ultra-performance liq. chromatog. coupled with quadrupole time of flight mass spectrometry (UPLC/QTOF-MS). Principal component anal. allowed a holistic approach in showing distinct chem. differences between the three ginseng species and correct classification of the two com. products to their resp. species. Further investigation of the chem. profile variations yielded ten main markers that were distinct for the three species. This study shows the potential of chem. profiling for the classification of complex natural product samples, such as ginseng, and application to com. products sold in the market. This methodol. can assist the industry in authenticating the various species of ginseng and providing a quick assessment of the quality of com. ginseng products.
- 56Kellogg, J. J.; Todd, D. A.; Egan, J. M.; Raja, H. A.; Oberlies, N. H.; Kvalheim, O. M.; Cech, N. B. J. Nat. Prod. 2016, 79, 376– 386 DOI: 10.1021/acs.jnatprod.5b01014Google Scholar56https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitVyhtbw%253D&md5=1b0e3c8c0b0651ffc3cf6b09147147f5Biochemometrics for Natural Products Research: Comparison of Data Analysis Approaches and Application to Identification of Bioactive CompoundsKellogg, Joshua J.; Todd, Daniel A.; Egan, Joseph M.; Raja, Huzefa A.; Oberlies, Nicholas H.; Kvalheim, Olav M.; Cech, Nadja B.Journal of Natural Products (2016), 79 (2), 376-386CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)A central challenge of natural products research is assigning bioactive compds. from complex mixts. The gold std. approach to address this challenge, bioassay-guided fractionation, is often biased toward abundant, rather than bioactive, mixt. components. This study evaluated the combination of bioassay-guided fractionation with untargeted metabolite profiling to improve active component identification early in the fractionation process. Key to this methodol. was statistical modeling of the integrated biol. and chem. data sets (biochemometric anal.). Three data anal. approaches for biochemometric anal. were compared, namely, partial least-squares loading vectors, S-plots, and the selectivity ratio. Exts. from the endophytic fungi Alternaria sp. and Pyrenochaeta sp. with antimicrobial activity against Staphylococcus aureus served as test cases. Biochemometric anal. incorporating the selectivity ratio performed best in identifying bioactive ions from these exts. early in the fractionation process, yielding altersetin (3, MIC 0.23 μg/mL) and macrosphelide A (4, MIC 75 μg/mL) as antibacterial constituents from Alternaria sp. and Pyrenochaeta sp., resp. This study demonstrates the potential of biochemometrics coupled with bioassay-guided fractionation to identify bioactive mixt. components. A benefit of this approach is the ability to integrate multiple stages of fractionation and bioassay data into a single anal.
- 57Pluskal, T.; Castillo, S.; Villar-Briones, A.; Orešič, M. BMC Bioinf. 2010, 11, 395 DOI: 10.1186/1471-2105-11-395Google Scholar57https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3cjjsVymsA%253D%253D&md5=e6e2ac996767f8526daccbdb7f4929e0MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile dataPluskal Tomas; Castillo Sandra; Villar-Briones Alejandro; Oresic MatejBMC bioinformatics (2010), 11 (), 395 ISSN:.BACKGROUND: Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. RESULTS: A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. CONCLUSIONS: MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
- 58Kvalheim, O. M.; Chan, H. Y.; Benzie, I. F. F.; Szeto, Y. T.; Tzang, A. H.; Mok, D. K.; Chau, F. Chemom. Intell. Lab. Syst. 2011, 107, 98– 105 DOI: 10.1016/j.chemolab.2011.02.002Google Scholar58https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXlslagtro%253D&md5=358e8e7bd62e2ecf1d8ecd955a0e5536Chromatographic profiling and multivariate analysis for screening and quantifying the contributions from individual components to the bioactive signature in natural productsKvalheim, Olav M.; Chan, Hoi-Yan; Benzie, Iris F. F.; Szeto, Yim-Tong; Tzang, Alexander Hing-Chung; Mok, Daniel Kam-Wah; Chau, Foo-TimChemometrics and Intelligent Laboratory Systems (2011), 107 (1), 98-105CODEN: CILSEN; ISSN:0169-7439. (Elsevier B.V.)A new approach for assigning bioactivity to individual components in exts. from natural products is presented and validated. 60 mixts. were created according to a uniform design from 12 chem. components of which 7 possessed antioxidant activity. The synthetic mixts. were characterized by chromatog. profiling and their antioxidant power was assessed by use of the Ferric Reducing Antioxidant Power (FRAP) assay. 40 of the prepd. mixts. were used as a training set to create a cross validated partial least squares (PLS) regression model with the FRAP measurement as response. The remaining 20 mixts. were used as an independent external validation set. The bioactive signature was singled out from the multi-component PLS model using target projection (TP). In addn. to excellent prediction performance of antioxidant strength from the bioactive signature, our approach, called Quant. Pattern-Activity Relationship (QPAR), was able to rank 6 of the 7 bioactive components according to individual bioactive strength. The ratios of bioactive capacity of the two most active components to the two least active components were close to 100 to 1. This explains why one of the two least bioactive components was not detected.
- 59Kvalheim, O. M.; Brakstad, F.; Liang, Y. Anal. Chem. 1994, 66, 43– 51 DOI: 10.1021/ac00073a010Google ScholarThere is no corresponding record for this reference.
- 60Bingol, K.; Bruschweiler, R. Anal. Chem. 2014, 86, 47– 57 DOI: 10.1021/ac403520jGoogle Scholar60https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhslaqu7jO&md5=3d24f8f88d8c0f3e8e6272f1de69dea6Multidimensional Approaches to NMR-Based MetabolomicsBingol, Kerem; Bruschweiler, RafaelAnalytical Chemistry (Washington, DC, United States) (2014), 86 (1), 47-57CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A review. The field of metabolomics, which is also referred to as metabonomics, has gained significant attention over the recent past as it is developing rapidly as a powerful way to comprehensively study complex biol. systems from a small mol. perspective. According to the Web of Science, since 2010 over 5,000 papers have been published with the key words "metabolomics", "metabonomics" or "metabolite profiling". Small biol. mols. (or metabolites) with mol. wt. <1500 Da are involved in many crit. functions in biol. systems, such as energetics, signaling, and as building blocks of more complex biopolymers, which makes the understanding of their compn., chem. structure, and reaction pathways important. Two main objectives of metabolic anal. are the discovery of modified and new natural products and the detection of biol. meaningful changes in metabolite concn. and fluxes.
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Abstract
Figure 1
Figure 1. Principal component analysis (PCA) scores plot of green tea samples drawn with Hotelling’s 95% confidence ellipse. Data points representing triplicate green tea samples were closely clustered, and distinct clusters were observed between green tea supplements, green teas, and the negative control (turmeric–ginger tea, T23, indicated in the figure as “non-green tea”). Representative samples are highlighted (T23, T24, T26, T27, and T37) to demonstrate the reproducibility of the extraction and analytical protocol. SRM represents standard reference material from the National Institute of Standards and Technology (NIST); data points indicated as “suppl” are green tea supplements.
Figure 2
Figure 2. Loadings plots from untargeted MS-based PCA of green tea samples. Metabolites with more negative correlation values along the x-axis (PC1, green labels) were present in higher concentrations in the green tea samples versus the negative control (T23, turmeric–ginger tea) and were responsible for the separation observed along the horizontal axis of Figure 1. Labeled metabolites with greater positive correlation along the y-axis (PC2, brown labels) were more heavily represented in green tea supplement samples versus the loose leaf green tea samples and were the dominant metabolites underlying the differentiation of the two sample groups in the vertical axis of Figure 1. Metabolites were identified by comparison against analytical standards. In cases where standards were not present, comparisons against the literature using m/z values from high-resolution mass spectrometry are provided. Identifications based on mass without reference standards are tentative.
Figure 3
Figure 3. Annotated mass spectral profile identifying green tea metabolites used in this study. (A) Positive electrospray ionization mode. (B) Negative electrospray ionization mode.
Figure 4
Figure 4. Quantification of green tea standards in tea samples. Boxes represent average concentrations of triplicate samples in μg/mg extract. #Negative control (turmeric–ginger tea); ‡NIST standard reference materials; §green teas containing other botanical additives. ext, extract.
Figure 5
Figure 5. Principal component analysis of targeted mass spectrometry data, drawn with Hotelling’s 95% confidence ellipse. The chemometric matrix consisted of 15 quantified samples (targeted variables) and 38 objects (for quantification data, see Table S3, Supporting Information). Representative samples are highlighted (T23, T26, T27, and T37).
Figure 6
Figure 6. Principal component analysis (PCA) scores plot of data from 1H NMR metabolomics analysis of green tea extracts. Representative samples are highlighted (T23, T26, T27, and T37).
Figure 7
Figure 7. Heat map correlation matrix for green tea samples. Correlation was based upon the averaged metabolomic profile for each sample and calculated from the reproduced correlation coefficient matrix comprised of a four principal component model (Table S5, Supporting Information). Darker shades represent stronger correlation between samples. #Negative control; ‡NIST standard reference materials; §green teas with botanical additives.
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- 3Reddy, K. R.; Belle, S. H.; Fried, M. W.; Afdhal, N.; Navarro, V. J.; Hawke, R. L.; Wahed, A. S.; Doo, E.; Meyers, C. M. Clin. Trials 2012, 9, 102– 112 DOI: 10.1177/1740774511427064Google Scholar3https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC38vnvFKrug%253D%253D&md5=dc27093bc0a5e5c03f76f21f96483b33Rationale, challenges, and participants in a Phase II trial of a botanical product for chronic hepatitis CReddy K Rajender; Belle Steven H; Fried Michael W; Afdhal Nezam; Navarro Victor J; Hawke Roy L; Wahed Abdus S; Doo Edward; Meyers Catherine MClinical trials (London, England) (2012), 9 (1), 102-12 ISSN:.BACKGROUND: Chronic hepatitis C is associated with significant morbidity and mortality as a consequence of progression to cirrhosis, hepatocellular carcinoma, and liver failure. Current treatment for chronic hepatitis C with pegylated interferon (IFN) and ribavirin is associated with suboptimal responses and numerous adverse effects. A number of botanical products have been used to treat hepatic disorders. Silymarin, extracted from the milk thistle plant, Silybum marianum (L) Gaertn. (Asteraceae), has been most widely used for various liver disorders, including chronic hepatitis C, B, and alcoholic liver disease. However, the safety and efficacy of silymarin have not been studied systematically in chronic hepatitis C. PURPOSE: We describe our strategy for a phased approach for studying the impact of silymarin in hepatitis C, in the context of the unique challenges of botanical product clinical trials and the development of specific and curative antiviral therapy. METHODS: This multicenter, randomized, double-masked, placebo-controlled trial was conducted with four clinical centers and a data-coordinating center in the United States, to assess the impact of silymarin therapy in patients with chronic hepatitis C who failed conventional antiviral therapy. RESULTS: Key aspects relevant to performing clinical trials of botanical products include early identification of an appropriate product with standard product chemistry, acquisition of pharmacokinetic and dosing information, selection of the appropriate study group, and choosing rigorous outcome variables. POTENTIAL LIMITATIONS: Trial participants were chronic hepatitis C patients who were nonsustained virologic responders to IFN-based therapy; therefore, the findings are not generalizable to all hepatitis C populations. Further, alanine aminotransferase, a biochemical liver test, rather than hepatitis viral RNA or liver histology was the primary end point. CONCLUSIONS: The challenges identified and addressed during development of this United States multicenter Phase II trial to evaluate silymarin for treatment of patients with chronic hepatitis C infection who had failed to respond successfully to previous IFN-based therapy are common and must be addressed to conduct rigorous trials of botanical products.
- 4Center for Drug Evaluation and Research. Botanical Drug Development: Guidance for Industry; U.S. Department of Health and Human Services, 2015.Google ScholarThere is no corresponding record for this reference.
- 5Cabrera, C.; Artacho, R.; Giménez, R. J. Am. Coll. Nutr. 2006, 25, 79– 99 DOI: 10.1080/07315724.2006.10719518Google Scholar5https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XksFSmurk%253D&md5=3fff5093d9e8966333451235e5d7627aBeneficial effects of green tea - a reviewCabrera, Carmen; Artacho, Reyes; Gimenez, RafaelJournal of the American College of Nutrition (2006), 25 (2), 79-99CODEN: JONUDL; ISSN:0731-5724. (American College of Nutrition)A review. Tea is the most consumed drink in the world after water. Green tea is a 'non-fermented' tea, and contains more catechins, than black tea or oolong tea. Catechins are in vitro and in vivo strong antioxidants. In addn., its content of certain minerals and vitamins increases the antioxidant potential of this type of tea. Since ancient times, green tea has been considered by the traditional Chinese medicine as a healthful beverage. Recent human studies suggest that green tea may contribute to a redn. in the risk of cardiovascular disease and some forms of cancer, as well as to the promotion of oral health and other physiol. functions such as anti-hypertensive effect, body wt. control, antibacterial and antiviral activity, solar UV protection, bone mineral d. increase, anti-fibrotic properties, and neuroprotective power. Increasing interest in its health benefits has led to the inclusion of green tea in the group of beverages with functional properties. However, although all the evidence from research on green tea is very promising, future studies are necessary to fully understand its contributions to human health, and advise its regular consumption in Western diets, in which green tea consumption is nowadays limited and sporadic.
- 6Smith, T.; Kawa, K.; Eckl, V.; Johnson, J. Herbalgram 2016, 111, 67– 73Google ScholarThere is no corresponding record for this reference.
- 7Bogdanski, P.; Suliburska, J.; Szulinska, M.; Stepien, M.; Pupek-Musialik, D.; Jablecka, A. Nutr. Res. (N. Y., NY, U. S.) 2012, 32, 421– 427 DOI: 10.1016/j.nutres.2012.05.007Google Scholar7https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XovVGksrw%253D&md5=6d47d41caf7d6d2a7d01d2ac9c0e0c94Green tea extract reduces blood pressure, inflammatory biomarkers, and oxidative stress and improves parameters associated with insulin resistance in obese, hypertensive patientsBogdanski, Pawel; Suliburska, Joanna; Szulinska, Monika; Stepien, Marta; Pupek-Musialik, Danuta; Jablecka, AnnaNutrition Research (New York, NY, United States) (2012), 32 (6), 421-427CODEN: NTRSDC; ISSN:0271-5317. (Elsevier)Green tea (GT) consumption is known to be assocd. with enhanced cardiovascular and metabolic health. The purpose of this study is to examine the hypothesis that supplementation with GT alters insulin resistance and assocd. cardiovascular risk factors in obese, hypertensive patients. In a double-blind, placebo-controlled trial, 56 obese, hypertensive subjects were randomized to receive a daily supplement of 1 capsule that contained either 379 mg of GT ext. (GTE) or a matching placebo, for 3 mo. At baseline and after 3 mo of treatment, the anthropometric parameters, blood pressure, plasma lipid levels, glucose levels, creatinine levels, tumor necrosis factor α levels, C-reactive protein levels, total antioxidant status, and insulin levels were assessed. Insulin resistance was evaluated according to the homeostasis model assessment-insulin resistance protocol. After 3 mo of supplementation, both systolic and diastolic blood pressures had significantly decreased in the GTE group as compared with the placebo group (P < .01). Considerable (P < .01) redns. in fasting serum glucose and insulin levels and insulin resistance were obsd. in the GTE group when compared with the placebo group. Serum tumor necrosis factor α and C-reactive protein were significantly lower, whereas total antioxidant status increased in the GTE group compared with the placebo (P < .05). Supplementation also contributed to significant (P < .05) decreases in the total and low-d. lipoprotein cholesterol and triglycerides, but an increase in high-d. lipoprotein cholesterol. In conclusion, daily supplementation with 379 mg of GTE favorably influences blood pressure, insulin resistance, inflammation and oxidative stress, and lipid profile in patients with obesity-related hypertension.
- 8Hsieh, S.-R.; Hsu, C.-S.; Lu, C.-H.; Chen, W.-C.; Chiu, C.-H.; Liou, Y.-M. J. Biomed. Sci. 2013, 20, 86 DOI: 10.1186/1423-0127-20-86Google Scholar8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhvV2nu7rP&md5=3ae17e86ba0498b4f98945243b52e708Epigallocatechin-3-gallate-mediated cardioprotection by Akt/GSK-3β/caveolin signalling in H9c2 rat cardiomyoblastsHsieh, Shih-Ron; Hsu, Chen-Sen; Lu, Chen-Hua; Chen, Wei-Cheng; Chiu, Chun-Hwei; Liou, Ying-MingJournal of Biomedical Science (London, United Kingdom) (2013), 20 (), 86CODEN: JBCIEA; ISSN:1423-0127. (BioMed Central Ltd.)Background: Epigallocatechin-3-gallate (EGCg) with its potent anti-oxidative capabilities is known for its beneficial effects ameliorating oxidative injury to cardiac cells. Although studies have provided convincing evidence to support the cardioprotective effects of EGCg, it remains unclear whether EGCg affect trans-membrane signalling in cardiac cells. Here, we have demonstrated the potential mechanism for cardioprotection of EGCg against H2O2-induced oxidative stress in H9c2 cardiomyoblasts. Results: Exposing H9c2 cells to H2O2 suppressed cell viability and altered the expression of adherens and gap junction proteins with increased levels of intracellular reactive oxygen species and cytosolic Ca2+. These detrimental effects were attenuated by pre-treating cells with EGCg for 30 min. EGCg also attenuated H2O2-mediated cell cycle arrest at the G1-S phase through the glycogen synthase kinase-3β (GSK-3β)/β-catenin/cyclin D1 signalling pathway. To det. how EGCg targets H9c2 cells, enhanced green fluorescence protein (EGFP) was ectopically expressed in these cells. EGFP-emission fluorescence spectroscopy revealed that EGCg induced dose-dependent fluorescence changes in EGFP expressing cells, suggesting that EGCg signalling events might trigger proximity changes of EGFP expressed in these cells. Proteomics studies showed that EGFP formed complexes with the 67 kD laminin receptor, caveolin-1 and -3, β-actin, myosin 9, vimentin in EGFP expressing cells. Using in vitro oxidative stress and in vivo myocardial ischemia models, we also demonstrated the involvement of caveolin in EGCg-mediated cardioprotection. In addn., EGCg-mediated caveolin-1 activation was found to be modulated by Akt/GSK-3β signalling in H2O2-induced H9c2 cell injury. Conclusions: Our data suggest that caveolin serves as a membrane raft that may help mediate cardioprotective EGCg transmembrane signalling.
- 9Patel, S. Food Sci. Technol. Res. 2013, 19, 923– 932 DOI: 10.3136/fstr.19.923Google Scholar9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXotFelsrc%253D&md5=07c9f18ff0fd4bcbd07bb74f8c04c1f5Green tea as a nutraceutical: the latest developmentsPatel, SeemaFood Science and Technology Research (2013), 19 (6), 923-932CODEN: FSTRFS; ISSN:1344-6606. (Japanese Society for Food Science and Technology)A review. Green tea processed from Camellia sinensis leaves is a common beverage with enormous medicinal importance. The accumulating evidences derived from in vitro, animal studies and human trials say loud for its diverse therapeutic potentials. The polyphenol rich tea ext. has been validated to offer benefits as cancer prevention, amelioration of diabetes side-effects, cardiovascular safety, cognitive boost, promotion of wt. loss, skin care, allergy suppression, protection from osteoarthritis, prebiotics etc. The flavanols, epigallocatechin gallate (EGCG) and epigallocatechin (EGC) have been identified to confer most of the biol. effects. This review has been composed to keep track of the recent breakthroughs in this rapidly evolving area of dietary supplements. Mechanisms of functionality have been mentioned where ever deemed essential.
- 10Liu, K.; Zhou, R.; Wang, B.; Chen, K.; Shi, L. Y.; Zhu, J. D.; Mi, M. T. Am. J. Clin. Nutr. 2013, 98, 340– 348 DOI: 10.3945/ajcn.112.052746Google Scholar10https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtFynsbnJ&md5=bf15b7037dfc619229e2da3c731feb10Effect of green tea on glucose control and insulin sensitivity: a meta-analysis of 17 randomized controlled trialsLiu, Kai; Zhou, Rui; Wang, Bin; Chen, Ka; Shi, Lin-Ying; Zhu, Jun-Dong; Mi, Man-TianAmerican Journal of Clinical Nutrition (2013), 98 (2), 340-348CODEN: AJCNAC; ISSN:0002-9165. (American Society for Nutrition)Background: The results of studies investigating the effect of green tea on glucose control and insulin sensitivity in humans are inconsistent. Objective: We aimed to quant. evaluate the effect of green tea on glucose control and insulin sensitivity. Design: We performed a strategic literature search of PubMed, EMBASE, and the Cochrane Library (updated to Jan. 2013) for randomized controlled trials that evaluated the effects of green tea and green tea ext. on glucose control and insulin sensitivity. Study quality was assessed by using the Jadad scale. Weighted mean differences were calcd. for net changes in glycemic measures by using fixed-effects or random-effects models. We conducted prespecified subgroup and sensitivity analyses to explore potential heterogeneity. Meta-regression analyses were conducted to investigate dose effects of green tea on fasting glucose and insulin concns. Results: Seventeen trials comprising a total of 1133 subjects were included in the current meta-anal. Green tea consumption significantly reduced the fasting glucose and Hb A1c (Hb A1c) concns. by -0.09 mmol/L (95% CI: -0.15, -0.03 mmol/L; P < 0.01) and -0.30% (95% CI: -0.37, -0.22%; P < 0.01), resp. Further stratified analyses from high Jadad score studies showed that green tea significantly reduced fasting insulin concns. (-1.16 μIU/mL; 95% CI: -1.91, -0.40 μIU/mL; P = 0.03). Conclusions: This meta-anal. suggested that green tea had favorable effects, ie, decreased fasting glucose and Hb A1c concns. Subgroup analyses showed a significant redn. in fasting insulin concns. in trials with high Jadad scores.
- 11Peng, X.; Zhou, R.; Wang, B.; Yu, X.; Yang, X.; Liu, K.; Mi, M. T. Sci. Rep. 2014, 4, 6251 DOI: 10.1038/srep06251Google Scholar11https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXksVyqt7k%253D&md5=db9185bde0323247e00da19c6e231b31Effect of green tea consumption on blood pressure: A meta-analysis of 13 randomized controlled trialsPeng, Xiaoli; Zhou, Rui; Wang, Bin; Yu, Xiaoping; Yang, Xiaohong; Liu, Kai; Mi, MantianScientific Reports (2014), 4 (), 6251CODEN: SRCEC3; ISSN:2045-2322. (Nature Publishing Group)The studies investigating the effects of green tea on blood pressure (BP) have generated inconsistent results. The aim of this study is to quant. evaluate the effects of green tea on BP control. PubMed, Embase, and the Cochrane Library (updated to March 2014) were searched for randomized controlled trials evaluating the effects of green tea on BP. Pooled effect of green tea consumption on BP was evaluated using fixed-effects or random-effects model. Thirteen trials comprising a total of 1,367 subjects were included in the current meta-anal. The overall outcome of the meta-anal. suggested that green tea consumption significantly decrease systolic blood pressure (SBP) level by -1.98 mmHg (95% CI: -2.94, -1.01 mmHg; P < 0.001). Compared with the control group, green tea also showed a significant lowering effect on diastolic blood pressure (DBP) in treatment group (-1.92 mmHg; 95% CI: -3.17, -0.68 mmHg; P = 0.002). Subgroup analyses further suggested that the pos. effect of green tea polyphenols on BP was only showed in studies using a low-dose green tea polyphenol, with the long-term intervention duration or ruling out the confounding effects of caffeine. The meta-anal. suggested that green tea consumption had a favorable effect on decrease of BP.
- 12Zheng, X. X.; Xu, Y. L.; Li, S. H.; Hui, R.; Wu, Y. J.; Huang, X. H. Am. J. Clin. Nutr. 2013, 97, 750– 762 DOI: 10.3945/ajcn.111.032573Google Scholar12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXlt1SqsbY%253D&md5=558ab0f7b84bcaddd91cd4201ea3fa0bEffects of green tea catechins with or without caffeine on glycemic control in adults: a meta-analysis of randomized controlled trialsZheng, Xin-Xin; Xu, Yan-Lu; Li, Shao-Hua; Hui, Rutai; Wu, Yong-Jian; Huang, Xiao-HongAmerican Journal of Clinical Nutrition (2013), 97 (4), 750-762CODEN: AJCNAC; ISSN:0002-9165. (American Society for Nutrition)Background: The effect of green tea catechins (GTCs) with or without caffeine on glycemic control is controversial. Objective: We aimed to identify and quantify the effects of GTCs or GTC-caffeine mixts. on glucose metab. in adults. Design: A comprehensive literature search was conducted to identify relevant trials of GTCs with or without caffeine on markers of glycemic control [fasting blood glucose (FBG), fasting blood insulin (FBI), glycated Hb (Hb A1c), and homeostatic model assessment of insulin resistance (HOMA-IR)]. Weighted mean differences were calcd. for net changes by using fixed-effects models. Prespecified subgroup analyses were performed to explore the influence of covariates on net changes in FBG and FBI concns. Results: Twenty-two eligible randomized controlled trials with 1584 subjects were identified. Pooled analyses showed that FBG (-1.48 mg/dL; 95% CI: -2.57, -0.40 mg/dL) decreased significantly with GTCs with or without caffeine, whereas FBI (0.04 μU/mL; 95% CI: -0.36, 0.45 μU/mL), Hb A1c (-0.04%; 95% CI: -0.15, 0.08%), and HOMA-IR (-0.05; 95% CI: -0.37, 0.26) did not. Subgroup analyses indicated that the glucose-lowering effect was apparent when the duration of follow-up was over a median of 12 wk. Overall, no significant heterogeneity was detected for FBG, FBI, Hb A1c, or HOMA-IR. Conclusions: The meta-anal. showed that the administration of GTCs with or without caffeine resulted in a significant redn. in FBG. The limited data available on GTCs did not support a pos. effect on FBI, Hb A1c, or HOMA-IR. Thus, more large and well-designed trials are needed in the future.
- 13Graham, H. N. Prev. Med. 1992, 21, 334– 350 DOI: 10.1016/0091-7435(92)90041-FGoogle ScholarThere is no corresponding record for this reference.
- 14Dwyer, J. T.; Peterson, J. Am. J. Clin. Nutr. 2013, 98, 1611S– 1618S DOI: 10.3945/ajcn.113.059584Google ScholarThere is no corresponding record for this reference.
- 15Coxon, D. T.; Holmes, A.; Ollis, W. D.; Vora, V. C.; Grant, M. S.; Tee, J. L. Tetrahedron 1972, 28, 2819– 2826 DOI: 10.1016/0040-4020(72)80118-2Google ScholarThere is no corresponding record for this reference.
- 16Wang, D.; Lu, J.; Miao, A.; Xie, Z.; Yang, D. J. Food Compos. Anal. 2008, 21, 361– 369 DOI: 10.1016/j.jfca.2008.01.002Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXntlSntLw%253D&md5=ddddbf6a4a3f8398abedf1cbd9dbaa25HPLC-DAD-ESI-MS/MS analysis of polyphenols and purine alkaloids in leaves of 22 tea cultivars in ChinaWang, Dongmei; Lu, Jiali; Miao, Aiqing; Xie, Zhiyong; Yang, DepoJournal of Food Composition and Analysis (2008), 21 (5), 361-369CODEN: JFCAEE; ISSN:0889-1575. (Elsevier B.V.)Using the HPLC-DAD-ESI-MS/MS method, polyphenols and purine alkaloids were analyzed in young leaves of 22 tea cultivars mainly for making oolong tea in China. Ultrasonic extn. in acetonitrile-water (1:1, vol./vol.) was chosen for the prepn. of samples for HPLC anal. Sepns. were performed on an ODS column with linear gradient elution by the mobile phase consisting of acetonitrile and 1% formic acid aq. soln. Compared with the stds. and data in the literature, 11 catechins, two purine alkaloids, and three non-catechin type of polyphenols were identified, including (-)-epigallocatechin-3-(3''-O-methyl) gallate, (-)-epigallocatechin-3,5-digallate, (-)-epicatechin-3-(3''-O-methyl) gallate, and (-)-epicatechin-3,5-digallate, which are not common catechins in all tea leaves. The contents of the major components, nine catechins and two purine alkaloids were quantified by the HPLC method which were fully validated. The compn. of catechins in the leaves of Fujian and Taiwan cultivars group was similar, while the contents of gallated catechins were higher in Guangdong cultivars group than in Fujian and Taiwan cultivars group.
- 17Jun, X.; Deji, S.; Ye, L.; Rui, Z. Int. J. Pharm. 2011, 408, 97– 101 DOI: 10.1016/j.ijpharm.2011.02.002Google Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3Mzot12jsQ%253D%253D&md5=99a956248540865ea61b8b7163b66fe4Comparison of in vitro antioxidant activities and bioactive components of green tea extracts by different extraction methodsJun Xi; Deji Shen; Ye Li; Rui ZhangInternational journal of pharmaceutics (2011), 408 (1-2), 97-101 ISSN:.In this study, in vitro antioxidant activities and bioactive components of green tea extracts (GTE) by ultrahigh pressure extraction and conventional extraction methods (microwave extraction, ultrasonic extraction, Soxhlet extraction and heat reflux extraction) were investigated. DPPH radical-scavenging and FTC method were applied to test the antioxidant activities. The bioactive components were determined by chemical methods. The results indicated that the GTE by ultrahigh pressure extraction exhibited the strongest antioxidant activities. The contents of polyphenols and catechins in the GTE by ultrahigh pressure extraction were significantly higher than those by other extraction methods, which was possibly responsible for the higher antioxidant activities of the GTE by ultrahigh pressure extraction. From the results we can draw the conclusion that not only the more bioactive components are obtained but also the extract has better free radical and reactive oxygen species scavenging activities through ultrahigh pressure extraction method. These findings further illustrate that ultrahigh pressure extraction has a bright prospect for extracting active ingredients from plant materials.
- 18Rusak, G.; Komes, D.; Likić, S.; Horžić, D.; Kovač, M. Food Chem. 2008, 110, 852– 858 DOI: 10.1016/j.foodchem.2008.02.072Google ScholarThere is no corresponding record for this reference.
- 19Balentine, D. A.; Dwyer, J. T.; Erdman, J. W., Jr.; Ferruzzi, M. G.; Gaine, C.; Harnly, J. M.; Kwik-Uribe, C. L. Am. J. Clin. Nutr. 2015, 101, 1113– 1125 DOI: 10.3945/ajcn.113.071274Google Scholar19https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVOjtbbE&md5=8bc0b222b663a02228fc54dcc634ff77Recommendations on reporting requirements for flavonoids in researchBalentine, Douglas A.; Dwyer, Johanna T.; Erdman, John W., Jr.; Ferruzzi, Mario G.; Gaine, P. Courtney; Harnly, James M.; Kwik-Uribe, Catherine L.American Journal of Clinical Nutrition (2015), 101 (6), 1113-1125CODEN: AJCNAC; ISSN:0002-9165. (American Society for Nutrition)Numerous observational and intervention-based human studies support the notion of a beneficial role for dietary flavonoids in human health. Despite these studies, it is not yet possible to make dietary recommendations with regard to the types and amts. of flavonoids to be consumed. The inherent diversity of flavonoid structure, chem., and natural distribution in foods lends itself to errors in reporting the types and/or amts. of flavonoids consumed, as well as incomplete recognition of requirements for intervention studies that aim to assess their benefits in a clin. setting. A need exists for guidelines that facilitate the design and reporting of flavonoid research. With a focus on clin. studies, this article (1) outlines limitations commonly encountered in the field of flavonoid research, including the inconsistent use of nomenclature, inappropriate analytic methods, inconsistent use of existing flavonoid databases, and the lack of full consideration in the design of test materials for intervention trials, and (2) provides guidance for future studies with a focus on clin. intervention trials. Adoption of this guidance will facilitate more accurate and interpretable research that will support the development of dietary recommendations regarding the intake of flavonoids.
- 20Sreekumar, A.; Poisson, L. M.; Rajendiran, T. M.; Khan, A. P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R. J.; Li, Y.; Nyati, M. K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G. S.; Ghosh, D.; Pennathur, S.; Alexander, D. C.; Berger, A.; Shuster, J. R.; Wei, J. T.; Varambally, S.; Beecher, C.; Chinnaiyan, A. M. Nature 2009, 457, 910– 914 DOI: 10.1038/nature07762Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhslCnt70%253D&md5=8f67dd4877a80cebd671c8d5de05bcdfMetabolomic profiles delineate potential role for sarcosine in prostate cancer progressionSreekumar, Arun; Poisson, Laila M.; Rajendiran, Thekkelnaycke M.; Khan, Amjad P.; Cao, Qi; Yu, Jindan; Laxman, Bharathi; Mehra, Rohit; Lonigro, Robert J.; Li, Yong; Nyati, Mukesh K.; Ahsan, Aarif; Kalyana-Sundaram, Shanker; Han, Bo; Cao, Xuhong; Byun, Jaeman; Omenn, Gilbert S.; Ghosh, Debashis; Pennathur, Subramaniam; Alexander, Danny C.; Berger, Alvin; Shuster, Jeffrey R.; Wei, John T.; Varambally, Sooryanarayana; Beecher, Christopher; Chinnaiyan, Arul M.Nature (London, United Kingdom) (2009), 457 (7231), 910-914CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)Multiple, complex mol. events characterize cancer development and progression. Deciphering the mol. networks that distinguish organ-confined disease from metastatic disease may lead to the identification of crit. biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumors, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination of high-throughput liq.-and-gas-chromatog.-based mass spectrometry, the authors profiled more than 1126 metabolites across 262 clin. samples related to prostate cancer (42 tissues and 110 each of urine and plasma). These unbiased metabolomic profiles were able to distinguish benign prostate, clin. localized prostate cancer and metastatic disease. Sarcosine, an N-Me deriv. of the amino acid glycine, was identified as a differential metabolite that was highly increased during prostate cancer progression to metastasis and can be detected non-invasively in urine. Sarcosine levels were also increased in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-Me transferase, the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addn. of exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degrdn., sarcosine dehydrogenase, induced an invasive phenotype in benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway. Here, by profiling the metabolomic alterations of prostate cancer progression, the authors reveal sarcosine as a potentially important metabolic intermediary of cancer cell invasion and aggressivity.
- 21Clayton, T. A.; Lindon, J. C.; Cloarec, O.; Antti, H.; Charuel, C.; Hanton, G.; Provost, J. P.; Le Net, J. L.; Baker, D.; Walley, R. J.; Everett, J. R.; Nicholson, J. K. Nature 2006, 440, 1073– 1077 DOI: 10.1038/nature04648Google ScholarThere is no corresponding record for this reference.
- 22Cotton, J.; Leroux, F.; Broudin, S.; Marie, M.; Corman, B.; Tabet, J.-C.; Ducruix, C.; Junot, C. J. Agric. Food Chem. 2014, 62, 11335– 11345 DOI: 10.1021/jf504400cGoogle ScholarThere is no corresponding record for this reference.
- 23Krug, D.; Müller, R. Nat. Prod. Rep. 2014, 31, 768– 783 DOI: 10.1039/c3np70127aGoogle Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXotFCitbc%253D&md5=e19c742793e6fa01a7f784f18910cd2fSecondary metabolomics: the impact of mass spectrometry-based approaches on the discovery and characterization of microbial natural productsKrug, Daniel; Muller, RolfNatural Product Reports (2014), 31 (6), 768-783CODEN: NPRRDF; ISSN:0265-0568. (Royal Society of Chemistry)A review. The in-depth anal. of secondary metabolomes of many microbes offers tremendous opportunities for the discovery of novel natural products which often exhibit promising biol. activities. However, over the last years the increasing availability of whole-genome information has led to raised expectations, as bioinformatic anal. revealed that traditional strategies to discover novel secondary metabolites apparently have so far only scratched the surface of the real microbial "secondary metabolome landscape". Metabolomics-based approaches using modern mass spectrometry techniques can help to bridge the gap between genome-encoded potential for the prodn. of secondary metabolites and the usually contradictory low nos. of compds. known from a specific producer. In this article recent studies are highlighted in which metabolomics-driven anal. played a crucial role for the discovery of novel secondary metabolites from microbial sources. The authors also exemplify how the implementation of metabolomics techniques facilitates the structural characterization of novel metabolites and contributes to the in-depth investigation of underlying biosynthetic pathways. Furthermore, the constantly increasing role of secondary metabolomics for the identification of novel natural products in a drug discovery context is discussed.
- 24Simmler, C.; Kulakowski, D.; Lankin, D. C.; McAlpine, J. B.; Chen, S. N.; Pauli, G. F. Adv. Nutr. 2016, 7, 179– 189 DOI: 10.3945/an.115.009928Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitVOjurvE&md5=3cfc648cb35b6cadfade91de685109dcHolistic analysis enhances the description of metabolic complexity in dietary natural productsSimmler, Charlotte; Kulakowski, Daniel; Lankin, David C.; McAlpine, James B.; Chen, Shao-Nong; Pauli, Guido F.Advances in Nutrition (2016), 7 (1), 179-189CODEN: ANDUAW; ISSN:2156-5376. (American Society for Nutrition)In the field of food and nutrition, complex natural products (NPs) are typically obtained from cells/tissues of diverse organisms such as plants, mushrooms, and animals. Among them, edible fruits, grains, and vegetables represent most of the human diet. Because of an important dietary dependence, the comprehensive metabolomic anal. of dietary NPs, performed holistically via the assessment of as many metabolites as possible, constitutes a fundamental building block for understanding the human diet. Both mass spectrometry (MS) and NMR (NMR) are important complementary analytic techniques, covering a wide range of metabolites at different concns. Particularly, 1-dimensional 1H-NMR offers an unbiased overview of all metabolites present in a sample without prior knowledge of its compn., thereby leading to an untargeted anal. In the past decade, NMR-based metabolomics in plant and food analyses has evolved considerably. The scope of the present review, covering literature of the past 5 y, is to address the relevance of 1H-NMR-based metabolomics in food plant studies, including a comparison with MS-based techniques. Major applications of NMR-based metabolomics for the quality control of dietary NPs and assessment of their nutritional values are presented.
- 25Oliver, S. G.; Winson, M. K.; Kell, D. B.; Baganz, F. Trends Biotechnol. 1998, 16, 373– 378 DOI: 10.1016/S0167-7799(98)01214-1Google ScholarThere is no corresponding record for this reference.
- 26Ku, K. M.; Choi, J. N.; Kim, J.; Kim, J. K.; Yoo, L. G.; Lee, S. J.; Hong, Y.-S.; Lee, C. H. J. Agric. Food Chem. 2010, 58, 418– 426 DOI: 10.1021/jf902929hGoogle ScholarThere is no corresponding record for this reference.
- 27Lee, J.-E.; Lee, B.-J.; Chung, J.-O.; Hwang, J.-A.; Lee, S.-J.; Lee, C.-H.; Hong, Y.-S. J. Agric. Food Chem. 2010, 58, 10582– 10589 DOI: 10.1021/jf102415mGoogle ScholarThere is no corresponding record for this reference.
- 28Lee, J.-E.; Lee, B.-J.; Chung, J.-O.; Kim, H. N.; Kim, E.-H.; Jung, S.; Lee, H.; Lee, S. J.; Hong, Y.-S. Food Chem. 2015, 174, 452– 459 DOI: 10.1016/j.foodchem.2014.11.086Google ScholarThere is no corresponding record for this reference.
- 29Zhang, A.; Sun, H.; Wang, P.; Han, Y.; Wang, X. Analyst 2012, 137, 293– 300 DOI: 10.1039/C1AN15605EGoogle ScholarThere is no corresponding record for this reference.
- 30Scalbert, A.; Brennan, L.; Manach, C.; Andres-Lacueva, C.; Dragsted, L. O.; Draper, J.; Rappaport, S. M.; van der Hooft, J. J. J.; Wishart, D. S. Am. J. Clin. Nutr. 2014, 99, 1286– 1308 DOI: 10.3945/ajcn.113.076133Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXovFGmsbs%253D&md5=8179a9816055829b29bbf4e2cc254973The food metabolome: a window over dietary exposureScalbert, Augustin; Brennan, Lorraine; Manach, Claudine; Andres-Lacueva, Cristina; Dragsted, Lars O.; Draper, John; Rappaport, Stephen M.; van der Hooft, Justin J. J.; Wishart, David S.American Journal of Clinical Nutrition (2014), 99 (6), 1286-1308CODEN: AJCNAC; ISSN:0002-9165. (American Society for Nutrition)A review. The food metabolome is defined as the part of the human metabolome directly derived from the digestion and biotransformation of foods and their constituents. With >25,000 compds. known in various foods, the food metabolome is extremely complex, with a compn. varying widely according to the diet. By its very nature it represents a considerable and still largely unexploited source of novel dietary biomarkers that could be used to measure dietary exposures with a high level of detail and precision. Most dietary biomarkers currently have been identified on the basis of our knowledge of food compns. by using hypothesis-driven approaches. However, the rapid development of metabolomics resulting from the development of highly sensitive modern analytic instruments, the availability of metabolite databases, and progress in (bio)informatics has made agnostic approaches more attractive as shown by the recent identification of novel biomarkers of intakes for fruit, vegetables, beverages, meats, or complex diets. Moreover, examples also show how the scrutiny of the food metabolome can lead to the discovery of bioactive mols. and dietary factors assocd. with diseases. However, researchers still face hurdles, which slow progress and need to be resolved to bring this emerging field of research to maturity. These limits were discussed during the First International Workshop on the Food Metabolome held in Glasgow. Key recommendations made during the workshop included more coordination of efforts; development of new databases, software tools, and chem. libraries for the food metabolome; and shared repositories of metabolomic data. Once achieved, major progress can be expected toward a better understanding of the complex interactions between diet and human health.
- 31Fujimura, Y.; Kurihara, K.; Ida, M.; Kosaka, R.; Miura, D.; Wariishi, H.; Maeda-Yamamoto, M.; Nesumi, A.; Saito, T.; Kanda, T.; Yamada, K.; Tachibana, H. PLoS One 2011, 6, e23426 DOI: 10.1371/journal.pone.0023426Google ScholarThere is no corresponding record for this reference.
- 32Cech, N. B.; Yu, K. LCGC North America 2013, 31, 938– 947Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXltV2ltg%253D%253D&md5=e36f33e7094c66512117978c53cdea6fMass spectrometry for natural products research: challenges, pitfalls, and opportunitiesCech, Nadja B.; Yu, KateLCGC North America (2013), 31 (11), 938, 940-947CODEN: LNACBH; ISSN:1527-5949. (Advanstar Communications, Inc.)A review. A common attitude in natural products research is that NMR (NMR) spectroscopy serves as a primary tool, whereas mass spectrometry (MS) is relegated to the task of providing the mol. formulas of pure compds. Yet over the past several decades, we have witnessed astonishing growth in MS. Electrospray ionization has enabled the anal. of biol. mols. previously deemed intractable, and instruments that offer astounding mass accuracy are becoming routinely available. Nonetheless, as applied to natural products research, MS is still fraught with challenges and pitfalls. Here is an account of strategies to conduct effective research despite these obstacles.
- 33Lee, J.-E.; Lee, B.-J.; Hwang, J.-A.; Ko, K.-S.; Chung, J.-O.; Kim, E.-H.; Lee, S.-J.; Hong, Y.-S. J. Agric. Food Chem. 2011, 59, 10579– 10585 DOI: 10.1021/jf202304zGoogle ScholarThere is no corresponding record for this reference.
- 34Machado, F. L. S.; Duarte, H. M.; Gestinari, L. M. S.; Cassano, V.; Kaiser, C. R.; Soares, A. R. Chem. Biodiversity 2016, 13, 845– 851 DOI: 10.1002/cbdv.201500246Google ScholarThere is no corresponding record for this reference.
- 35Siheri, W.; Zhang, T.; Ebiloma, G. U.; Biddau, M.; Woods, N.; Hussain, M. Y.; Clements, C. J.; Fearnley, J.; Ebel, R. E.; Paget, T.; Muller, S.; Carter, K. C.; Ferro, V. A.; De Koning, H. P.; Watson, D. G. PLoS One 2016, 11, 1– 16 DOI: 10.1371/journal.pone.0155355Google ScholarThere is no corresponding record for this reference.
- 36Mushtaq, M. Y.; Choi, Y. H.; Verpoorte, R.; Wilson, E. G. Phytochem. Anal. 2014, 25, 291– 306 DOI: 10.1002/pca.2505Google Scholar36https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXitlSitbo%253D&md5=cad0a3ff9eee87268369460a70834a25Extraction for Metabolomics: Access to The MetabolomeMushtaq, Mian Yahya; Choi, Young Hae; Verpoorte, Robert; Wilson, Erica G.Phytochemical Analysis (2014), 25 (4), 291-306CODEN: PHANEL; ISSN:0958-0344. (John Wiley & Sons Ltd.)A review. The value of information obtained from a metabolomic study depends on how much of the metabolome is present in analyzed samples. Thus, only a comprehensive and reproducible extn. method will provide reliable data because the metabolites that will be measured are those that were extd. and all conclusions will be built around this information. The objective was to discuss the efficiency and reliability of available sample pre-treatment methods and their application in different fields of metabolomics. The review has three sections: the first deals with pre-extn. techniques, the second discusses the choice of extn. solvents and their main features and the third includes a brief description of the most used extn. techniques: microwave-assisted extn., solid-phase extn., supercrit. fluid extn., Soxhlet and a new method developed in our lab. - the comprehensive extn. method. ResultsExamn. of over 200 studies showed that sample collection, homogenisation, grinding and storage could affect the yield and reproducibility of results. They also revealed that apart from the solvent used for extn., the extn. techniques have a decisive role on the metabolites available for anal. It is essential to evaluate efficacy and reproducibility of sample pre-treatment as a first step to ensure the reliability of a metabolomic study. Among the reviewed methods, the comprehensive extn. method appears to provide a promising approach for extg. diverse types of metabolites. Copyright © 2014 John Wiley & Sons, Ltd.
- 37Amazon.com Amazon Best Sellers. http://www.amazon.com/Best-Sellers-Grocery-Gourmet-Food-Green-Tea-Beverages/zgbs/grocery/16318471 (November 11, 2015) ,.Google ScholarThere is no corresponding record for this reference.
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- 39Health, J.Green Tea Supplement Reviews. http://www.benefitsofgreentea.info/2007/green-tea-supplement-reviews.php (November 11, 2015) .Google ScholarThere is no corresponding record for this reference.
- 40Ku, K. M.; Kim, J.; Park, H. J.; Liu, K. H.; Lee, C. H. J. Agric. Food Chem. 2010, 58, 345– 352 DOI: 10.1021/jf902818cGoogle ScholarThere is no corresponding record for this reference.
- 41Lee, L.-S.; Choi, J. H.; Son, N.; Kim, S.-H.; Park, J.-D.; Jang, D.-J.; Jeong, Y.; Kim, H.-J. J. Agric. Food Chem. 2013, 61, 332– 338 DOI: 10.1021/jf304161yGoogle ScholarThere is no corresponding record for this reference.
- 42Sang, S.; Lee, M.-H.; Ho, C.-T.; Yang, C. S. J. Agric. Food Chem. 2005, 53, 9478– 9484 DOI: 10.1021/jf0519055Google ScholarThere is no corresponding record for this reference.
- 43Nishimura, M.; Ishiyama, K.; Watanabe, A.; Kawano, S.; Miyase, T.; Sano, M. J. Agric. Food Chem. 2007, 55, 7252– 7257 DOI: 10.1021/jf070312mGoogle ScholarThere is no corresponding record for this reference.
- 44Yokozawa, T.; Nakagawa, T. Food Chem. Toxicol. 2004, 42, 975– 981 DOI: 10.1016/j.fct.2004.02.010Google Scholar44https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXjsVWhsb0%253D&md5=159d711e8ff5acc6db90dda96ebfb781Inhibitory effects of Luobuma tea and its components against glucose-mediated protein damageYokozawa, Takako; Nakagawa, TakakoFood and Chemical Toxicology (2004), 42 (6), 975-981CODEN: FCTOD7; ISSN:0278-6915. (Elsevier Science B.V.)Luobuma tea, prepd. from the leaves of Apocynum venetum L., is a popular beverage in China. In this study, the activity of Luobuma leaf ext. and its components against the formation of advanced glycation endproducts (AGEs), which are largely involved in the pathogenesis of diabetic vascular complications, was examd. using the in vitro glycation reaction. Strong inhibitory activity against the formation of AGEs was shown by Luobuma aq. ext. Following further fractionation of this ext., seven polyphenolic compds., i.e. (±)-gallocatechin, (-)-epigallocatechin, (±)-catechin, (-)-epicatechin, epicatechin-(4β-8)-gallocatechin, epigallocatechin-(4β-8)-epicatechin and procyanidin B-2, were isolated by Sephadex LH-20 column chromatog. These purified compds. also exerted inhibitory activities that were more potent than the pos. control, aminoguanidine. Our findings may help to explain the beneficial effects of this plant against atherosclerosis.
- 45National Institute of Standards and Technology, Standard Reference Material 3254,Certificate of Analysis. In (2016.Google ScholarThere is no corresponding record for this reference.
- 46Rothwell, J. A.; Pérez-Jiménez, J.; Neveu, V.; Medina-Ramon, A.; M’Hiri, N.; Garcia Lobato, P.; Manach, C.; Knox, K.; Eisner, R.; Wishart, D.; Scalbert, A. Database 2013) 2013, bat070 DOI: 10.1093/database/bat070Google ScholarThere is no corresponding record for this reference.
- 47Zhao, Y.; Chen, P.; Lin, L.; Harnly, J. M.; Liangli, Y.; Li, Z. Food Chem. 2011, 126, 1269– 1277 DOI: 10.1016/j.foodchem.2010.11.055Google ScholarThere is no corresponding record for this reference.
- 48Yuk, J.; McIntyre, K. L.; Fischer, C.; Hicks, J.; Colson, K. L.; Lui, E.; Brown, D.; Arnason, J. T. Anal. Bioanal. Chem. 2013, 405, 4499– 4509 DOI: 10.1007/s00216-012-6582-6Google Scholar48https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhvVGqs7bE&md5=c8423d8f0a1a1a34291f68e23e9385baDistinguishing Ontario ginseng landraces and ginseng species using NMR-based metabolomicsYuk, Jimmy; McIntyre, Kristina L.; Fischer, Christian; Hicks, Joshua; Colson, Kimberly L.; Lui, Ed; Brown, Dan; Arnason, John T.Analytical and Bioanalytical Chemistry (2013), 405 (13), 4499-4509CODEN: ABCNBP; ISSN:1618-2642. (Springer)The use of 1H-NMR-based metabolomics to distinguish and identify unique markers of five Ontario ginseng (Panax quinquefolius) landraces and two ginseng species (P. quinquefolius and P. ginseng) was evaluated. Three landraces (2, 3, and 5) were distinguished from one another in the principal component anal. (PCA) scores plot. Further anal. was conducted and specific discriminating metabolites from the PCA loadings were detd. Landraces 3 and 5 were distinguishable on the basis of a decreased NMR intensity in the Me ginsenoside region, indicating decreased overall ginsenoside levels. In addn., landrace 5 was sepd. by an increased amt. of sucrose relative to the rest of the landraces. Landrace 2 was sepd. from the rest of the landraces by the increased level of ginsenoside Rb1. The Ontario P. quinquefolius was also compared with Asian P. ginseng by PCA, and clear sepn. between the two groups was detected in the PCA scores plot. The PCA loadings plot and a t-test NMR difference plot were able to identify an increased level of maltose and a decreased level of sucrose in the Asian ginseng compared with the Ontario ginseng. An overall decrease of ginsenoside content, esp. ginsenoside Rb1, was also detected in the Asian ginseng's metabolic profile. This study demonstrates the potential of NMR-based metabolomics as a powerful high-throughput technique in distinguishing various closely related ginseng landraces and its ability to identify metabolic differences from Ontario and Asian ginseng. The results from this study will allow better understanding for quality assessment, species authentication, and the potential for developing a fully automated method for quality control.
- 49Lee, J.-E.; Lee, B.-J.; Chung, J.-O.; Shin, H.-J.; Lee, S.-J.; Lee, C.-H.; Hong, Y.-S. Food Res. Int. 2011, 44, 597– 604 DOI: 10.1016/j.foodres.2010.12.004Google Scholar49https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXjs1CmtLo%253D&md5=4eda441cad6318cf8fed6b7d253b10f11H NMR-based metabolomic characterization during green tea (Camellia sinensis) fermentationLee, Jang-Eun; Lee, Bum-Jin; Chung, Jin-Oh; Shin, Hyun-Jung; Lee, Sang-Jun; Lee, Cherl-Ho; Hong, Young-ShickFood Research International (2011), 44 (2), 597-604CODEN: FORIEU; ISSN:0963-9969. (Elsevier B.V.)The metabolic behavior of green tea (Camellia sinensis) during tea fermn. was characterized by 1H NMR spectroscopy coupled with multivariate statistical anal. to provide comprehensive information on changes in metabolites induced by tea fermn. Fourteen tea metabolites of epicatechin (EC), epigallocatechin (EGC), epicatechin-3-gallate (ECG), epigallocatechin-3-gallate (EGCG), theanine, alanine, acetate, quinate, glutamate, caffeine, sucrose, glucose, and gallate, as identified by 1H NMR spectroscopy, were responsible for metabolic differentiation between green tea and fermented tea by principal component anal. During tea fermn., levels of EC, EGC, ECG, EGCG, quinate, caffeine, and sucrose were decreased, whereas gallate and glucose levels were increased. In particular, unique changes in caffeine and gallate levels were obsd. during tea fermn., which caffeine and gallate levels have been shown to vary after tea fermn. among many reports to date. This study highlights that metabolomics with global profiling and a highly reliable and reproducible 1H NMR spectroscopic data set can provide a better understanding of unique changes in tea metabolites during tea fermn.
- 50Farag, M. A.; Porzel, A.; Al-Hammady, M. A.; Hegazy, M.-E. F.; Meyer, A.; Mohamed, T. A.; Westphal, H.; Wessjohann, L. A. J. Proteome Res. 2016, 15, 1274– 1287 DOI: 10.1021/acs.jproteome.6b00002Google Scholar50https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xislait7Y%253D&md5=af43b6fb212490b53f7521ebf1033297Soft Corals Biodiversity in the Egyptian Red Sea: A Comparative MS and NMR Metabolomics Approach of Wild and Aquarium Grown SpeciesFarag, Mohamed A.; Porzel, Andrea; Al-Hammady, Montasser A.; Hegazy, Mohamed-Elamir F.; Meyer, Achim; Mohamed, Tarik A.; Westphal, Hildegard; Wessjohann, Ludger A.Journal of Proteome Research (2016), 15 (4), 1274-1287CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)Marine life has developed unique metabolic and physiol. capabilities and advanced symbiotic relationships to survive in the varied and complex marine ecosystems. Herein, metabolite compn. of the soft coral genus Sarcophyton was profiled with respect to its species and different habitats along the coastal Egyptian Red Sea via 1H NMR and ultra performance liq. chromatog.-mass spectrometry (UPLC-MS) large-scale metabolomics analyses. The current study extends the application of comparative secondary metabolite profiling from plants to corals revealing for metabolite compositional differences among its species via a comparative MS and NMR approach. This was applied for the first time to investigate the metab. of 16 Sarcophyton species in the context of their genetic diversity or growth habitat. Under optimized conditions, we were able to simultaneously identify 120 metabolites including 65 diterpenes, 8 sesquiterpenes, 18 sterols, and 15 oxylipids. Principal component anal. (PCA) and orthogonal projection to latent structures-discriminant anal. (OPLS) were used to define both similarities and differences among samples. For a compd. based classification of coral species, UPLC-MS was found to be more effective than NMR. The main differentiations emanate from cembranoids and oxylipids. The specific metabolites that contribute to discrimination between soft corals of S. ehrenbergi from the three different growing habitats also belonged to cembrane type diterpenes, with aquarium S. ehrenbergi corals being less enriched in cembranoids compared to sea corals. PCA using either NMR or UPLC-MS data sets was found equally effective in predicting the species origin of unknown Sarcophyton. Cyclopropane contg. sterols obsd. in abundance in corals may act as cellular membrane protectant against the action of coral toxins, i.e., cembranoids.
- 51Farag, M. A.; Porzel, A.; Wessjohann, L. A. Phytochemistry 2012, 76, 60– 72 DOI: 10.1016/j.phytochem.2011.12.010Google ScholarThere is no corresponding record for this reference.
- 52Saldanha, L.; Dwyer, J. T.; Andrews, K.; Betz, J.; Harnly, J.; Pehrsson, P.; Rimmer, C.; Savarala, S. J. Food Sci. 2015, 80, H883– H888 DOI: 10.1111/1750-3841.12838Google ScholarThere is no corresponding record for this reference.
- 53Bajpai, V.; Singh, A.; Arya, K. R.; Srivastava, M.; Kumar, B. Food Addit. Contam., Part A 2015, 32, 799– 807 DOI: 10.1080/19440049.2015.1022885Google ScholarThere is no corresponding record for this reference.
- 54Rocha, T.; Amaral, J. S.; Oliveira, M. B. P. P. Compr. Rev. Food Sci. Food Saf. 2016, 15, 43– 62 DOI: 10.1111/1541-4337.12173Google ScholarThere is no corresponding record for this reference.
- 55Yuk, J.; Patel, D. N.; Isaac, G.; Smith, K.; Wrona, M.; Olivos, H. J.; Yu, K. J. Braz. Chem. Soc. 2016, 27, 1476– 1483 DOI: 10.5935/0103-5053.20160189Google Scholar55https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xhsl2nt73N&md5=d43d3750a5ca951c07ee7e73a11f0970Chemical profiling of ginseng species and ginseng herbal products using UPLC/QTOF-MSYuk, Jimmy; Patel, Dhavalkumar N.; Isaac, Giorgis; Smith, Kerri; Wrona, Mark; Olivos, Hernando J.; Yu, KateJournal of the Brazilian Chemical Society (2016), 27 (8), 1476-1483CODEN: JOCSET; ISSN:0103-5053. (Sociedade Brasileira de Quimica)The chem. profiles of four ginseng roots samples from three species of ginseng (Panax quinquefolius, Panax ginseng and Panax notoginseng) and two com. ginseng products contg. P. quinquefolius and red P. ginseng were compared using ultra-performance liq. chromatog. coupled with quadrupole time of flight mass spectrometry (UPLC/QTOF-MS). Principal component anal. allowed a holistic approach in showing distinct chem. differences between the three ginseng species and correct classification of the two com. products to their resp. species. Further investigation of the chem. profile variations yielded ten main markers that were distinct for the three species. This study shows the potential of chem. profiling for the classification of complex natural product samples, such as ginseng, and application to com. products sold in the market. This methodol. can assist the industry in authenticating the various species of ginseng and providing a quick assessment of the quality of com. ginseng products.
- 56Kellogg, J. J.; Todd, D. A.; Egan, J. M.; Raja, H. A.; Oberlies, N. H.; Kvalheim, O. M.; Cech, N. B. J. Nat. Prod. 2016, 79, 376– 386 DOI: 10.1021/acs.jnatprod.5b01014Google Scholar56https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitVyhtbw%253D&md5=1b0e3c8c0b0651ffc3cf6b09147147f5Biochemometrics for Natural Products Research: Comparison of Data Analysis Approaches and Application to Identification of Bioactive CompoundsKellogg, Joshua J.; Todd, Daniel A.; Egan, Joseph M.; Raja, Huzefa A.; Oberlies, Nicholas H.; Kvalheim, Olav M.; Cech, Nadja B.Journal of Natural Products (2016), 79 (2), 376-386CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)A central challenge of natural products research is assigning bioactive compds. from complex mixts. The gold std. approach to address this challenge, bioassay-guided fractionation, is often biased toward abundant, rather than bioactive, mixt. components. This study evaluated the combination of bioassay-guided fractionation with untargeted metabolite profiling to improve active component identification early in the fractionation process. Key to this methodol. was statistical modeling of the integrated biol. and chem. data sets (biochemometric anal.). Three data anal. approaches for biochemometric anal. were compared, namely, partial least-squares loading vectors, S-plots, and the selectivity ratio. Exts. from the endophytic fungi Alternaria sp. and Pyrenochaeta sp. with antimicrobial activity against Staphylococcus aureus served as test cases. Biochemometric anal. incorporating the selectivity ratio performed best in identifying bioactive ions from these exts. early in the fractionation process, yielding altersetin (3, MIC 0.23 μg/mL) and macrosphelide A (4, MIC 75 μg/mL) as antibacterial constituents from Alternaria sp. and Pyrenochaeta sp., resp. This study demonstrates the potential of biochemometrics coupled with bioassay-guided fractionation to identify bioactive mixt. components. A benefit of this approach is the ability to integrate multiple stages of fractionation and bioassay data into a single anal.
- 57Pluskal, T.; Castillo, S.; Villar-Briones, A.; Orešič, M. BMC Bioinf. 2010, 11, 395 DOI: 10.1186/1471-2105-11-395Google Scholar57https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3cjjsVymsA%253D%253D&md5=e6e2ac996767f8526daccbdb7f4929e0MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile dataPluskal Tomas; Castillo Sandra; Villar-Briones Alejandro; Oresic MatejBMC bioinformatics (2010), 11 (), 395 ISSN:.BACKGROUND: Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. RESULTS: A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. CONCLUSIONS: MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
- 58Kvalheim, O. M.; Chan, H. Y.; Benzie, I. F. F.; Szeto, Y. T.; Tzang, A. H.; Mok, D. K.; Chau, F. Chemom. Intell. Lab. Syst. 2011, 107, 98– 105 DOI: 10.1016/j.chemolab.2011.02.002Google Scholar58https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXlslagtro%253D&md5=358e8e7bd62e2ecf1d8ecd955a0e5536Chromatographic profiling and multivariate analysis for screening and quantifying the contributions from individual components to the bioactive signature in natural productsKvalheim, Olav M.; Chan, Hoi-Yan; Benzie, Iris F. F.; Szeto, Yim-Tong; Tzang, Alexander Hing-Chung; Mok, Daniel Kam-Wah; Chau, Foo-TimChemometrics and Intelligent Laboratory Systems (2011), 107 (1), 98-105CODEN: CILSEN; ISSN:0169-7439. (Elsevier B.V.)A new approach for assigning bioactivity to individual components in exts. from natural products is presented and validated. 60 mixts. were created according to a uniform design from 12 chem. components of which 7 possessed antioxidant activity. The synthetic mixts. were characterized by chromatog. profiling and their antioxidant power was assessed by use of the Ferric Reducing Antioxidant Power (FRAP) assay. 40 of the prepd. mixts. were used as a training set to create a cross validated partial least squares (PLS) regression model with the FRAP measurement as response. The remaining 20 mixts. were used as an independent external validation set. The bioactive signature was singled out from the multi-component PLS model using target projection (TP). In addn. to excellent prediction performance of antioxidant strength from the bioactive signature, our approach, called Quant. Pattern-Activity Relationship (QPAR), was able to rank 6 of the 7 bioactive components according to individual bioactive strength. The ratios of bioactive capacity of the two most active components to the two least active components were close to 100 to 1. This explains why one of the two least bioactive components was not detected.
- 59Kvalheim, O. M.; Brakstad, F.; Liang, Y. Anal. Chem. 1994, 66, 43– 51 DOI: 10.1021/ac00073a010Google ScholarThere is no corresponding record for this reference.
- 60Bingol, K.; Bruschweiler, R. Anal. Chem. 2014, 86, 47– 57 DOI: 10.1021/ac403520jGoogle Scholar60https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhslaqu7jO&md5=3d24f8f88d8c0f3e8e6272f1de69dea6Multidimensional Approaches to NMR-Based MetabolomicsBingol, Kerem; Bruschweiler, RafaelAnalytical Chemistry (Washington, DC, United States) (2014), 86 (1), 47-57CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A review. The field of metabolomics, which is also referred to as metabonomics, has gained significant attention over the recent past as it is developing rapidly as a powerful way to comprehensively study complex biol. systems from a small mol. perspective. According to the Web of Science, since 2010 over 5,000 papers have been published with the key words "metabolomics", "metabonomics" or "metabolite profiling". Small biol. mols. (or metabolites) with mol. wt. <1500 Da are involved in many crit. functions in biol. systems, such as energetics, signaling, and as building blocks of more complex biopolymers, which makes the understanding of their compn., chem. structure, and reaction pathways important. Two main objectives of metabolic anal. are the discovery of modified and new natural products and the detection of biol. meaningful changes in metabolite concn. and fluxes.
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