Co-reporter:Takuma Misawa;Yasuhiro Date
Journal of Proteome Research March 6, 2015 Volume 14(Issue 3) pp:1526-1534
Publication Date(Web):2017-2-22
DOI:10.1021/pr501194k
Daily intake information is important for an understanding of the metabolic fluctuation of humans exposed to environmental stimuli. However, little investigation has been performed on the variations in dietary intake as an input and the relationship with human fecal, urinary, and salivary metabolic fluctuations as output information triggered by daily dietary intake. In the present study, we describe a data-driven approach for visualizing the daily intake information on a nutritional scale and for evaluating input–output responses under uncontrolled diets in a human study. For the input evaluation of nutritional intake, we collected information about daily dietary intake and converted this information to numeric data of nutritional elements. Furthermore, for the evaluation of output metabolic, mineral, and microbiota responses, we characterized the metabolic, mineral, and microbiota variations of noninvasive human samples of feces, urine, and saliva. The data-driven approach captured significant differences in the fluctuation of intestinal microbiota and some metabolites caused by a high-protein and a high-fat diet in daily life. This approach should contribute to the metabolic assessment of humans affected by environmental and nutritional factors under unlimited and uncontrolled diets.Keywords: comprehensive significant test; data-driven approach; dietary intake variations; metabonomics; noninvasive human sampling; nutrient digitalization;
Co-reporter:Jun Kikuchi;Shunji Yamada
Analyst (1876-Present) 2017 vol. 142(Issue 22) pp:4161-4172
Publication Date(Web):2017/11/06
DOI:10.1039/C7AN01019B
NMR offers tremendous advantages in the analyses of molecular complexity, such as crude bio-fluids, bio-extracts, and intact cells and tissues. Here we introduce recent applications of NMR approaches, as well as next generation sequencing (NGS), for the evaluation of human and environmental health (i.e., maintenance of a homeostatic state) based on metabolic and microbial profiling and data science. We describe useful databases and web tools that are used to support these studies by facilitating the characterization of metabolites from complex NMR spectra. Because the NMR spectra of metabolic mixtures can produce numerical matrix data (e.g., chemical shift versus intensity) with high reproducibility and inter-institution convertibility, advanced data science approaches, such as multivariate analysis and machine learning, are desirable; therefore, we also introduce informatics techniques derived from heterogeneously measured data, such as environmental microbiota, for the extraction of submerged information using data science approaches. We summarize recent studies of microbiomes that are based on these techniques and show that, particularly in human studies, NMR-based metabolic characterization of non-invasive samples, such as feces, can provide a large quantity of beneficial information regarding human health and disease.
Co-reporter:Takuma Misawa, Takanori Komatsu, Yasuhiro Date and Jun Kikuchi
Chemical Communications 2016 vol. 52(Issue 14) pp:2964-2967
Publication Date(Web):11 Jan 2016
DOI:10.1039/C5CC09442A
The method provided here can overcome the low S/N problem in 13C NMR, by the integration of plural spectra to take advantage of high-resolution potential based on non-bucketing analysis without additional measurements. In addition, a new metabolite annotation approach using advanced STOCSY and quantum chemistry calculations was introduced in this study.
Co-reporter:Jun Kikuchi, Yuuri Tsuboi, Keiko Komatsu, Masahiro Gomi, Eisuke Chikayama, and Yasuhiro Date
Analytical Chemistry 2016 Volume 88(Issue 1) pp:659
Publication Date(Web):December 1, 2015
DOI:10.1021/acs.analchem.5b02311
A new Web-based tool, SpinCouple, which is based on the accumulation of a two-dimensional (2D) 1H–1H J-resolved NMR database from 598 metabolite standards, has been developed. The spectra include both J-coupling and 1H chemical shift information; those are applicable to a wide array of spectral annotation, especially for metabolic mixture samples that are difficult to label through the attachment of 13C isotopes. In addition, the user-friendly application includes an absolute-quantitative analysis tool. Good agreement was obtained between known concentrations of 20-metabolite mixtures versus the calibration curve-based quantification results obtained from 2D-Jres spectra. We have examined the web tool availability using nine series of biological extracts, obtained from animal gut and waste treatment microbiota, fish, and plant tissues. This web-based tool is publicly available via http://emar.riken.jp/spincpl.
Co-reporter:Yuka Shiokawa, Takuma Misawa, Yasuhiro Date, and Jun Kikuchi
Analytical Chemistry 2016 Volume 88(Issue 5) pp:2714
Publication Date(Web):January 29, 2016
DOI:10.1021/acs.analchem.5b04182
With the innovation of high-throughput metabolic profiling methods such as nuclear magnetic resonance (NMR), data mining techniques that can reveal valuable information from substantial data sets are constantly desired in this field. In particular, for the analytical assessment of various human lifestyles, advanced computational methods are ultimately needed. In this study, we applied market basket analysis, which is generally applied in social sciences such as marketing, and used transaction data derived from dietary intake information and urinary chemical data generated using NMR and inductively coupled plasma optical emission spectrometry measurements. The analysis revealed several relationships, such as fish diets with high trimethylamine N-oxide excretion and N-methylnicotinamide excreted at higher levels in the morning and produced from a protein that was consumed one day prior. Therefore, market basket analysis can be applied to metabolic profiling to effectively understand the relationships between metabolites and lifestyle.
Co-reporter:Takuma Misawa, Feifei Wei, and Jun Kikuchi
Analytical Chemistry 2016 Volume 88(Issue 12) pp:6130
Publication Date(Web):June 3, 2016
DOI:10.1021/acs.analchem.6b01495
Nuclear magnetic resonance (NMR) spectroscopy has tremendous advantages of minimal sample preparation and interconvertibility of data among different institutions; thus, large data sets are frequently acquired in metabolomics studies. Previously, we used a novel analytical strategy, named signal enhancement by spectral integration (SENSI), to overcome the low signal-to-noise ratio (S/N ratio) problem in 13C NMR by integration of hundreds of spectra without additional measurements. In this letter, the development of a SENSI 2D method and application to >1000 2D JRES NMR spectra are described. Remarkably, the obtained SENSI 2D spectrum had an approximate 14-fold increase in the S/N ratio and 80–250 additional peaks without any additional measurements. These results suggest that SENSI 2D is a useful method for assigning weak signals and that the use of coefficient of variation values can support the assignment information and extraction of features from the population characteristics among large data sets.
Co-reporter:Kengo Ito, Yu Tsutsumi, Yasuhiro Date, and Jun Kikuchi
ACS Chemical Biology 2016 Volume 11(Issue 4) pp:1030
Publication Date(Web):January 20, 2016
DOI:10.1021/acschembio.5b00894
The abundant observation of chemical fragment information for molecular complexities is a major advantage of biological NMR analysis. Thus, the development of a novel technique for NMR signal assignment and metabolite identification may offer new possibilities for exploring molecular complexities. We propose a new signal assignment approach for metabolite mixtures by assembling H–H, H–C, C–C, and Q–C fragmental information obtained by multidimensional NMR, followed by the application of graph and network theory. High-speed experiments and complete automatic signal assignments were achieved for 12 combined mixtures of 13C-labeled standards. Application to a 13C-labeled seaweed extract showed 66 H–C, 60 H–H, 326 C–C, and 28 Q–C correlations, which were successfully assembled to 18 metabolites by the automatic assignment. The validity of automatic assignment was supported by quantum chemical calculations. This new approach can predict entire metabolite structures from peak networks of biological extracts.
Co-reporter:Takanori Komatsu;Risa Ohishi;Amiu Shino ;Dr. Jun Kikuchi
Angewandte Chemie International Edition 2016 Volume 55( Issue 20) pp:6000-6003
Publication Date(Web):
DOI:10.1002/anie.201600334
Abstract
Improved signal identification for biological small molecules (BSMs) in a mixture was demonstrated by using multidimensional NMR on samples from 13C-enriched Rhododendron japonicum (59.5 atom%) cultivated in air containing 13C-labeled carbon dioxide for 14 weeks. The resonance assignment of 386 carbon atoms and 380 hydrogen atoms in the mixture was achieved. 42 BSMs, including eight that were unlisted in the spectral databases, were identified. Comparisons between the experimental values and the 13C chemical shift values calculated by density functional theory supported the identifications of unlisted BSMs. Tracing the 13C/12C ratio by multidimensional NMR spectra revealed faster and slower turnover ratios of BSMs involved in central metabolism and those categorized as secondary metabolites, respectively. The identification of BSMs and subsequent flow analysis provided insight into the metabolic systems of the plant.
Co-reporter:Eisuke Chikayama, Yudai Shimbo, Keiko Komatsu, and Jun Kikuchi
The Journal of Physical Chemistry B 2016 Volume 120(Issue 14) pp:3479-3487
Publication Date(Web):March 10, 2016
DOI:10.1021/acs.jpcb.5b12748
NMR spectroscopy is a powerful method for analyzing metabolic mixtures. The information obtained from an NMR spectrum is in the form of physical parameters, such as chemical shifts, and construction of databases for many metabolites will be useful for data interpretation. To increase the accuracy of theoretical chemical shifts for development of a database for a variety of metabolites, the effects of sets of conformations (structural ensembles) and the levels of theory on computations of theoretical chemical shifts were systematically investigated for a set of 29 small molecules in the present study. For each of the 29 compounds, 101 structures were generated by classical molecular dynamics at 298.15 K, and then theoretical chemical shifts for 164 1H and 123 13C atoms were calculated by ab initio quantum chemical methods. Six levels of theory were used by pairing Hartree–Fock, B3LYP (density functional theory), or second order Møller–Plesset perturbation with 6-31G or aug-cc-pVDZ basis set. The six average fluctuations in the 1H chemical shift were ±0.63, ± 0.59, ± 0.70, ± 0.62, ± 0.75, and ±0.66 ppm for the structural ensembles, and the six average errors were ±0.34, ± 0.27, ± 0.32, ± 0.25, ± 0.32, and ±0.25 ppm. The results showed that chemical shift fluctuations with changes in the conformation because of molecular motion were larger than the differences between computed and experimental chemical shifts for all six levels of theory. In conclusion, selection of an appropriate structural ensemble should be performed before theoretical chemical shift calculations for development of an accurate database for a variety of metabolites.
Co-reporter:Takanori Komatsu;Risa Ohishi;Amiu Shino ;Dr. Jun Kikuchi
Angewandte Chemie 2016 Volume 128( Issue 20) pp:6104-6107
Publication Date(Web):
DOI:10.1002/ange.201600334
Abstract
Improved signal identification for biological small molecules (BSMs) in a mixture was demonstrated by using multidimensional NMR on samples from 13C-enriched Rhododendron japonicum (59.5 atom%) cultivated in air containing 13C-labeled carbon dioxide for 14 weeks. The resonance assignment of 386 carbon atoms and 380 hydrogen atoms in the mixture was achieved. 42 BSMs, including eight that were unlisted in the spectral databases, were identified. Comparisons between the experimental values and the 13C chemical shift values calculated by density functional theory supported the identifications of unlisted BSMs. Tracing the 13C/12C ratio by multidimensional NMR spectra revealed faster and slower turnover ratios of BSMs involved in central metabolism and those categorized as secondary metabolites, respectively. The identification of BSMs and subsequent flow analysis provided insight into the metabolic systems of the plant.
Co-reporter:Feifei Wei, Kengo Ito, Kenji Sakata, Yasuhiro Date, and Jun Kikuchi
Analytical Chemistry 2015 Volume 87(Issue 5) pp:2819
Publication Date(Web):February 3, 2015
DOI:10.1021/ac504211n
Extracting useful information from high dimensionality and large data sets is a major challenge for data-driven approaches. The present study was aimed at developing novel integrated analytical strategies for comprehensively characterizing seaweed similarities based on chemical diversity. The chemical compositions of 107 seaweed and 2 seagrass samples were analyzed using multiple techniques, including Fourier transform infrared (FT-IR) and solid- and solution-state nuclear magnetic resonance (NMR) spectroscopy, thermogravimetry-differential thermal analysis (TG-DTA), inductively coupled plasma-optical emission spectrometry (ICP-OES), CHNS/O total elemental analysis, and isotope ratio mass spectrometry (IR-MS). The spectral data were preprocessed using non-negative matrix factorization (NMF) and NMF combined with multivariate curve resolution-alternating least-squares (MCR-ALS) methods in order to separate individual component information from the overlapping and/or broad spectral peaks. Integrated analysis of the preprocessed chemical data demonstrated distinct discrimination of differential seaweed species. Further network analysis revealed a close correlation between the heavy metal elements and characteristic components of brown algae, such as cellulose, alginic acid, and sulfated mucopolysaccharides, providing a componential basis for its metal-sorbing potential. These results suggest that this integrated analytical strategy is useful for extracting and identifying the chemical characteristics of diverse seaweeds based on large chemical data sets, particularly complicated overlapping spectral data.
Co-reporter:Tatsuki Ogura, Yasuhiro Date, Yuuri Tsuboi, and Jun Kikuchi
ACS Chemical Biology 2015 Volume 10(Issue 8) pp:1908
Publication Date(Web):May 22, 2015
DOI:10.1021/cb500609p
A new metabolic dynamics analysis approach has been developed in which massive data sets from time-series of 1H and 13C NMR spectra are integrated in combination with microbial variability to characterize the biomass degradation process using field soil microbial communities. On the basis of correlation analyses that revealed relationships between various metabolites and bacteria, we efficiently monitored the metabolic dynamics of saccharides, amino acids, and organic acids, by assessing time-course changes in the microbial and metabolic profiles during biomass degradation. Specific bacteria were found to support specific steps of metabolic pathways in the degradation process of biomass to short chain fatty acids. We evaluated samples from agricultural and abandoned fields contaminated by the tsunami that followed the Great East earthquake in Japan. Metabolic dynamics and activities in the biomass degradation process differed considerably between soil from agricultural and abandoned fields. In particular, production levels of short chain fatty acids, such as acetate and propionate, which were considered to be produced by soil bacteria such as Sedimentibacter sp. and Coprococcus sp., were higher in the soil from agricultural fields than from abandoned fields. Our approach could characterize soil activity based on the metabolic dynamics of microbial communities in the biomass degradation process and should therefore be useful in future investigations of the environmental effects of natural disasters on soils.
Co-reporter:Takanori Komatsu, Toshiya Kobayashi, Minoru Hatanaka, and Jun Kikuchi
Environmental Science & Technology 2015 Volume 49(Issue 11) pp:7056
Publication Date(Web):May 14, 2015
DOI:10.1021/acs.est.5b00837
Planktonic metabolism plays crucial roles in Earth’s elemental cycles. Chemical speciation as well as elemental stoichiometry is important for advancing our understanding of planktonic roles in biogeochemical cycles. In this study, a multicomponent solid-state nuclear magnetic resonance (NMR) approach is proposed for chemical speciation of cellular components, using several advanced NMR techniques. Measurements by ssNMR were performed on 13C and 15N-labeled Euglena gracilis, a flagellated protist. 3D dipolar-assisted rotational resonance, double-cross-polarization 1H–13C correlation spectroscopy, and 1H–13C solid-state heteronuclear single quantum correlation spectroscopy successively allowed characterization of cellular components. These techniques were then applied to E. gracilis cultured in high and low ammonium media to demonstrate the power of this method for profiling and comparing cellular components. Cellular NMR spectra indicated that ammonium induced both paramylon degradation and amination. Arginine was stored as a nitrogen reserve and ammonium replaced by arginine catabolism via the arginine dihydrolase pathway. 15N and 31P cellular ssNMR indicated arginine and polyphosphate accumulation in E. gracilis, respectively. This chemical speciation technique will contribute to environmental research by providing detailed information on environmental chemical properties.
Co-reporter:Kengo Ito, Kenji Sakata, Yasuhiro Date, and Jun Kikuchi
Analytical Chemistry 2014 Volume 86(Issue 2) pp:1098
Publication Date(Web):January 8, 2014
DOI:10.1021/ac402869b
Biological information is intricately intertwined with several factors. Therefore, comprehensive analytical methods such as integrated data analysis, combining several data measurements, are required. In this study, we describe a method of data preprocessing that can perform comprehensively integrated analysis based on a variety of multimeasurement of organic and inorganic chemical data from Sargassum fusiforme and explore the concealed biological information by statistical analyses with integrated data. Chemical components including polar and semipolar metabolites, minerals, major elemental and isotopic ratio, and thermal decompositional data were measured as environmentally responsive biological data in the seasonal variation. The obtained spectral data of complex chemical components were preprocessed to isolate pure peaks by removing noise and separating overlapping signals using the multivariate curve resolution alternating least-squares method before integrated analyses. By the input of these preprocessed multimeasurement chemical data, principal component analysis and self-organizing maps of integrated data showed changes in the chemical compositions during the mature stage and identified trends in seasonal variation. Correlation network analysis revealed multiple relationships between organic and inorganic components. Moreover, in terms of the relationship between metal group and metabolites, the results of structural equation modeling suggest that the structure of alginic acid changes during the growth of S. fusiforme, which affects its metal binding ability. This integrated analytical approach using a variety of chemical data can be developed for practical applications to obtain new biochemical knowledge including genetic and environmental information.
Co-reporter:Taiga Asakura, Yasuhiro Date, and Jun Kikuchi
Analytical Chemistry 2014 Volume 86(Issue 11) pp:5425
Publication Date(Web):May 14, 2014
DOI:10.1021/ac5005037
Estuarine environments accumulate large quantities of organic matter from land masses adjoining the sea, and this is consumed as part of the detritus cycle. These environments are rich in biodiversity, and their ecosystem services greatly benefit humans. However, the estuarine environments have complicated aqueous ecosystems, thus the comprehensive evaluation of biotic interactions and stability is difficult using conventional hypothesis-driven approaches. In this study, we describe the advancement of an evaluation strategy for characterizing and visualizing the interactions and relationships among the microorganisms and chemicals in sediment ecosystems of estuarine environments by a combination of organic matter and elemental profiling as well as microbial profiling. We also report our findings from a comparative analysis of estuarine and coastal environmental samples collected from the Kanto and Tsunami-affected Tohoku regions in Japan. The microbial-gated correlation deployed from the coefficient of microbiota from the correlation matrix and network analysis was able to visualize and summarize the different relationships among the microbial communities, sediment organic matter, and element profiles based on geographical differences in Kanto and Tohoku regions. We demonstrated remarkable estuarine eutrophication in the Kanto region based on abundant sediment polypeptide signals and water nitrogen ions catabolized by microbiota. Therefore, we propose that this data-driven approach is a powerful method for analyzing, visualizing, and evaluating complex metabolic dynamics and networks in sediment microbial ecosystems and can be applied to other environmental ecosystems, such as deep sea sediments and agronomic and forest soils.
Co-reporter:Yasuhiro Date, Yumiko Nakanishi, Shinji Fukuda, Yumi Nuijima, Tamotsu Kato, Mikihisa Umehara, Hiroshi Ohno, Jun Kikuchi
Food Chemistry 2014 Volume 152() pp:251-260
Publication Date(Web):1 June 2014
DOI:10.1016/j.foodchem.2013.11.126
•In vitro screening method for potential prebiotic foods was developed.•The JBOVS, JBO, and onion were nominated as candidate prebiotic foods.•Significant quantities of fructose-based carbohydrates were present in the JBOVS.•Increasing of acetate and lactate production was observed by JBOVS intake.•The JBOVS modulated the activities of the microbial community.The aim of this work was to develop a simple and rapid in vitro evaluation method for screening and discovery of uncharacterised and untapped prebiotic foods. Using a NMR-based metabolomic approach coupled with multivariate statistical analysis, the metabolic profiles generated by intestinal microbiota after in vitro incubation with feces were examined. The viscous substances of Japanese bunching onion (JBOVS) were identified as one of the candidate prebiotic foods by this in vitro screening method. The JBOVS were primarily composed of sugar components, especially fructose-based carbohydrates. Our results suggested that ingestion of JBOVS contributed to lactate and acetate production by the intestinal microbiota, and were accompanied by an increase in the Lactobacillus murinus and Bacteroidetes sp. populations in the intestine and fluctuation of the host-microbial co-metabolic process. Therefore, our approach should be useful as a rapid and simple screening tool for potential prebiotic foods.
Co-reporter:Takanori Komatsu and Jun Kikuchi
Analytical Chemistry 2013 Volume 85(Issue 18) pp:8857
Publication Date(Web):September 6, 2013
DOI:10.1021/ac402197h
A multidimensional solution NMR method has been developed using various pulse programs including HCCH-COSY and 13C-HSQC-NOESY for the structural characterization of commercially available 13C labeled lignocellulose from potatoes (Solanum tuberosum L.), chicory (Cichorium intybus), and corn (Zea mays). This new method allowed for 119 of the signals in the 13C-HSQC spectrum of lignocelluloses to be assigned and was successfully used to characterize the structures of lignocellulose samples from three plants in terms of their xylan and xyloglucan structures, which are the major hemicelluloses in angiosperm. Furthermore, this new method provided greater insight into fine structures of lignin by providing a high resolution to the aromatic signals of the β-aryl ether and resinol moieties, as well as the diastereomeric signals of the β-aryl ether. Finally, the 13C chemical shifts assigned in this study were compared with those from solid-state NMR and indicated the presence of heterogeneous dynamics in the polysaccharides where rigid cellulose and mobile hemicelluloses moieties existed together.
Co-reporter:Takanori Komatsu and Jun Kikuchi
The Journal of Physical Chemistry Letters 2013 Volume 4(Issue 14) pp:2279-2283
Publication Date(Web):June 25, 2013
DOI:10.1021/jz400978g
Solid-state dipolar dephasing filtered (DDF)-INADEQUATE experiments were used to detect the hemicellulosic signals of lignocellulosic mixtures; here dipolar dephasing was used as a signal filter to remove signals derived from cellulose. The maximum filtering efficiency was obtained when the dephasing time was adjusted to half the rotor period at a magic-angle spinning (MAS) frequency of 12 kHz, which indicated that the molecular motions in hemicelluloses are faster than those in cellulose. In a DDF-INADEQUATE spectrum of uniformly 13C-labeled lignocellulose from corn (Zea mays) collected with a dephasing time of 1/2νMAS, the chemical shifts of β-d-xylopyranose (Xylp) and α-l-arabinofuranose (Araf) in glucuronoarabinoxylan, the major hemicellulose in the secondary cell walls of the gramineous plant, were assigned.Keywords: biomass; molecular motion; NMR spectroscopy; signal separation; spin relaxation;
Co-reporter:Akira Yamazawa, Yasuhiro Date, Keijiro Ito, Jun Kikuchi
Journal of Bioscience and Bioengineering (March 2014) Volume 117(Issue 3) pp:305-309
Publication Date(Web):1 March 2014
DOI:10.1016/j.jbiosc.2013.08.010
Microbial ecosystems are typified by diverse microbial interactions and competition. Consequently, the microbial networks and metabolic dynamics of bioprocesses catalyzed by these ecosystems are highly complex, and their visualization is regarded as essential to bioengineering technology and innovation. Here we describe a means of visualizing the variants in a microbial community and their metabolic profiles. The approach enables previously unidentified bacterial functions in the ecosystems to be elucidated. We investigated the anaerobic bioremediation of chlorinated ethene in a soil column experiment as a case study. Microbial community and dechlorination profiles in the ecosystem were evaluated by denaturing gradient gel electrophoresis (DGGE) fingerprinting and gas chromatography, respectively. Dechlorination profiles were obtained from changes in dechlorination by microbial community (evaluated by data mining methods). Individual microbes were then associated with their dechlorination profiles by heterogenous correlation analysis. Our correlation-based visualization approach enables deduction of the roles and functions of bacteria in the dechlorination of chlorinated ethenes. Because it estimates functions and relationships between unidentified microbes and metabolites in microbial ecosystems, this approach is proposed as a control-logic tool by which to understand complex microbial processes.
Co-reporter:Takuma Misawa, Takanori Komatsu, Yasuhiro Date and Jun Kikuchi
Chemical Communications 2016 - vol. 52(Issue 14) pp:NaN2967-2967
Publication Date(Web):2016/01/11
DOI:10.1039/C5CC09442A
The method provided here can overcome the low S/N problem in 13C NMR, by the integration of plural spectra to take advantage of high-resolution potential based on non-bucketing analysis without additional measurements. In addition, a new metabolite annotation approach using advanced STOCSY and quantum chemistry calculations was introduced in this study.
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