• KOREAN JOURNAL OF CROP SCIENCE
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• v.63 no.4
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• pp.360-377
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• 2018

The levels of 12 isoflavones were measured in soybean (Glycine max (L.) Merrill) sprouts of 68 genetic varieties from three countries (China, Japan, and Korea). The isoflavone profile differences were analyzed using data mining methods. A principal component analysis (PCA) revealed that the CSRV021 variety was separated from the others by the first two principal components. This variety appears to be most suited for functional food production due to its high isoflavone levels. Partial least squares discriminant analysis (PLS-DA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) showed that there are meaningful isoflavone compositional differences in samples that have different countries of origin. Hierarchical clustering analysis (HCA) of these phytochemicals resulted in clusters derived from closely related biochemical pathways. These results indicate the usefulness of metabolite profiling combined with chemometrics as a tool for assessing the quality of foods and identifying metabolic links in biological systems.

• Journal of the Korean Magnetic Resonance Society
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• v.21 no.4
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• pp.119-125
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• 2017

The plucking season of green tea leaves is one of the important parameters that decide their metabolic diversity, quality, and prices. The effects of plucking sghlwleasons on green tea metabolites were investigated through metabolite profiling by $^1H$ NMR spectroscopy. The orthogonal projection on latent structure-discriminant analysis (OPLS-DA) showed clear discriminations of green teas by three different grades depending on plucking seasons: Ujeon, Sejak, and Jungjak. These results suggested that the nine peak groups could be used for diagnostics for identification of high quality Ujeon grade of green tea.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.11
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• pp.1633-1642
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• 2017

Objective: Evaluation of exercise effects in racehorses is important in horseracing industry and animal health care. In this study, we compared metabolic patterns between before and after exercise to screen metabolic biomarkers for exercise effects in thoroughbreds. Methods: The concentration of metabolites in muscle, plasma, and urine was measured by $^1H$ nuclear magnetic resonance (NMR) spectroscopy analysis and the relative metabolite levels in the three samples were compared between before and after exercise. Subsequently, multivariate data analysis based on the metabolic profiles was performed using orthogonal partial least square discriminant analysis (OPLS-DA) and variable important plots and t-test was used for basic statistical analysis. Results: From $^1H$ NMR spectroscopy analysis, 35, 25, and 34 metabolites were detected in the muscle, plasma, and urine. Aspartate, betaine, choline, cysteine, ethanol, and threonine were increased over 2-fold in the muscle; propionate and trimethylamine were increased over 2-fold in the plasma; and alanine, glycerol, inosine, lactate, and pyruvate were increased over 2-fold whereas acetoacetate, arginine, citrulline, creatine, glutamine, glutarate, hippurate, lysine, methionine, phenylacetylglycine, taurine, trigonelline, trimethylamine, and trimethylamine N-oxide were decreased below 0.5-fold in the urine. The OPLS-DA showed clear separation of the metabolic patterns before and after exercise in the muscle, plasma, and urine. Statistical analysis showed that after exercise, acetoacetate, arginine, glutamine, hippurate, phenylacetylglycine trimethylamine, trimethylamine N-oxide, and trigonelline were significantly decreased and alanine, glycerol, inosine, lactate, and pyruvate were significantly increased in the urine (p<0.05). Conclusion: In conclusion, we analyzed integrated metabolic patterns in the muscle, plasma, and urine before and after exercise in racehorses. We found changed patterns of metabolites in the muscle, plasma, and urine of racehorses before and after exercise.

• Allergy, Asthma & Immunology Research
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• v.10 no.6
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• pp.628-647
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• 2018

Purpose: Obesity is associated with metabolic dysregulation, but the underlying metabolic signatures involving clinical and inflammatory profiles of obese asthma are largely unexplored. We aimed at identifying the metabolic signatures of obese asthma. Methods: Eligible subjects with obese (n = 11) and lean (n = 22) asthma underwent body composition and clinical assessment, sputum induction, and blood sampling. Sputum supernatant was assessed for interleukin $(IL)-1{\beta}$, -4, -5, -6, -13, and tumor necrosis factor $(TNF)-{\alpha}$, and serum was detected for leptin, adiponectin and C-reactive protein. Untargeted gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based metabolic profiles in sputum, serum and peripheral blood monocular cells (PBMCs) were analyzed by orthogonal projections to latent structures-discriminate analysis (OPLS-DA) and pathway topology enrichment analysis. The differential metabolites were further validated by correlation analysis with body composition, and clinical and inflammatory profiles. Results: Body composition, asthma control, and the levels of $IL-1{\beta}$, -4, -13, leptin and adiponectin in obese asthmatics were significantly different from those in lean asthmatics. OPLS-DA analysis revealed 28 differential metabolites that distinguished obese from lean asthmatic subjects. The validation analysis identified 18 potential metabolic signatures (11 in sputum, 4 in serum and 2 in PBMCs) of obese asthmatics. Pathway topology enrichment analysis revealed that cyanoamino acid metabolism, caffeine metabolism, alanine, aspartate and glutamate metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, pentose phosphate pathway in sputum, and glyoxylate and dicarboxylate metabolism, glycerolipid metabolism and pentose phosphate pathway in serum are suggested to be significant pathways related to obese asthma. Conclusions: GC-TOF-MS-based metabolomics indicates obese asthma is characterized by a metabolic profile different from lean asthma. The potential metabolic signatures indicated novel immune-metabolic mechanisms in obese asthma with providing more phenotypic and therapeutic implications, which needs further replication and validation.

• Journal of Ginseng Research
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• v.37 no.3
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• pp.341-348
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• 2013

Identification of the origins of Panax ginseng has been issued in Korea scientifically and economically. We describe a metabolomics approach used for discrimination and prediction of ginseng roots from different origins in Korea. The fresh ginseng roots from six ginseng cooperative associations (Gangwon, Gaeseong, Punggi, Chungbuk, Jeonbuk, and Anseong) were analyzed by UPLC-MS-based approach combined with orthogonal projections to latent structure-discriminant analysis multivariate analysis. The ginsengs from Gangwon and Gaeseong were easily differentiated. We further analyzed the metabolomics results in subgroups. Punggi, Chungbuk, Jeonbuk, and Anseong ginseng could be easily differentiated by the first two orthogonal components. As a validation of the discrimination model, we performed blind prediction tests of sample origins using an external test set. Our model predicted their geographical origins as 99.7% probability. The robust discriminatory power and statistical validity of our method suggest its general applicability for determining the origins of P. ginseng samples.

• Bulletin of the Korean Chemical Society
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• v.34 no.5
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• pp.1467-1472
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• 2013

Metabolomics is a field that studies systematic dynamics and secretion of metabolites from cells to understand biological pathways based on metabolite changes. The metabolic profiling of intact human colorectal tissues was performed using high-resolution magic angle spinning (HR-MAS) NMR spectroscopy, which was unnecessary to extract metabolites from tissues. We used two different groups of samples, which were defined as normal and cancer, from 9 patients with colorectal cancer and investigated the samples in NMR experiments with a water suppression pulse sequence. We applied target profiling and multivariative statistical analysis to the analyzed 1D NMR spectra to identify the metabolites and discriminate between normal and cancer tissues. Cancer tissue showed higher levels of arginine, betaine, glutamate, lysine, taurine and lower levels of glutamine, hypoxanthine, isoleucine, lactate, methionine, pyruvate, tyrosine relative to normal tissue. In the OPLS-DA (orthogonal partial least square discriminant analysis), the score plot showed good separation between the normal and cancer groups. These results suggest that metabolic profiling of colorectal cancer could provide new biomarkers.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.419-424
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• 2014

Metabolite profiling of blueberry (cultivar "Spartan") was performed by extraction using different solvents, methanol and ethyl acetate, through metabolomic analysis using LC-MS/MS. Unsupervised classification method (PCA) and supervised prediction model (OPLS-DA) provided good categorization of metabolites according to the extraction solvents. Metabolites of the anthocyanin family, including delphinidin hexoside, delphinidin, 5-O-feruloylquinic acid, malvidin hexoside, malvidin-3-arabinoside, petunidin-3-arabinoside, and petunidin hexoside, were mainly detected in methanol fractions, whereas those of the flavonoid family, including chlorogenic acid, chlorogenic acid dimer, 6,8-di-C-arabinopyranosyl-luteolin, and luteolin were successfully prepared in the ethyl acetate fraction. Thus, metabolomic analysis of blueberry extracts allows for the simple profiling of whole and distinctive metabolites for future applications.

• Proceedings of the Microbiological Society of Korea Conference
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• 2009.05a
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• pp.78-78
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• 2009

$^1H$ NMR spectroscopy coupled with multivariate statistical analysis was used for the first time to investigate metabolic changes in musts during alcoholic fermentation and wines during ageing. Three Saccharomyces cerevisiae yeast strains (RC-212, KIV-1116 and KUBY-501) were also evaluated for their impacts on the metabolic changes in must and wine. Pattern recognition (PR) methods, including PCA, PLS-DA and OPLS-DA scores plots, showed clear differences for metabolites among musts or wines for each fermentation stage up to 6 months. Metabolites responsible for the differentiation were identified to valine, 2,3-butanediol (2,3-BD), pyruvate, succinate, proline, citrate, glycerol, malate, tartarate, glucose, N-methylnicotinic acid (NMNA), and polyphenol compounds. PCA scores plots showed continuous movements away from days 1 to 8 in all musts for all yeast strains, indicating continuous and active fermentation. During alcoholic fermentation, highest levels of 2,3-BD, succinate and glycerol were found in musts with the KIV-1116 strain, which showed the fastest fermentation or highest fermentative activity of the 3 strains, whereas the KUBY-501 strain showed the slowest fermentative activity. This study highlights the applicability of NMR-based metabolomics for monitoring wine fermentation and evaluating the fermentative characteristics of yeast strains.

• Korean Journal of Agricultural Science
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• v.45 no.3
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• pp.447-454
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• 2018

Zearalenone (ZEN), a mycotoxin produced by Fusarium in food and feed, causes serious damage to the health of humans and livestock. Therefore, we compared the metabolomic profiles in the liver, serum, and urine of piglets fed a ZEN-contaminated diet using proton nuclear magnetic resonance ($^1H-NMR$) spectroscopy. The spectra from the three different samples, treated with ZEN concentrations of 0.8 mg/kg for 4 weeks, were aligned and identified using MATLAB. The aligned data were subjected to discriminating analysis using multivariate statistical analysis and a web server for metabolite set enrichment analysis. The ZEN-exposed groups were almost separated in the three different samples. Metabolic analysis showed that 28, 29, and 20 metabolites were profiled in the liver, serum, and urine, respectively. The discriminating analysis showed that the alanine, arginine, choline, and glucose concentrations were increased in the liver. Phenylalanine and tyrosine metabolites showed high concentrations in serum, whereas valine showed a low concentration. In addition, the formate levels were increased in the ZEN-treated urine. For the integrated analysis, glucose, lactate, taurine, glycine, alanine, glutamate, glutamine, and creatine from orthogonal partial least squares discriminant analysis (OPLS-DA) were potential compounds for the discriminating analysis. In conclusion, our findings suggest that potential biomarker compounds can provide a better understanding on how ZEN contaminated feed in swine affects the liver, serum, and urine.

• Biomolecules & Therapeutics
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• v.21 no.6
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• pp.470-475
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• 2013

Ultraviolet (UV) radiation is a major environmental factor that leads to acute and chronic reactions in the human skin. UV exposure induces wrinkle formation, DNA damage, and generation of reactive oxygen species (ROS). Most mechanistic studies of skin physiology and pharmacology related with UV-irradiated skin have focused on proteins and their related gene expression or single-targeted small molecules. The present study identified and analyzed the alteration of skin metabolites following UVB irradiation and topical retinyl palmitate (RP, 5%) treatment in hairless mice using direct analysis in real time (DART) time-of-flight mass spectrometry (TOF-MS) with multivariate analysis. Under the negative ion mode, the DART ion source successfully ionized various fatty acids including palmitoleic and linolenic acid. From DART-TOF-MS fingerprints measured in positive mode, the prominent dehydrated ion peak (m/z: 369, M+H-$H_2O$) of cholesterol was characterized in all three groups. In positive mode, the discrimination among three groups was much clearer than that in negative mode by using multivariate analysis of orthogonal partial-least squares-discriminant analysis (OPLS-DA). DART-TOF-MS can ionize various small organic molecules in living tissues and is an efficient alternative analytical tool for acquiring full chemical fingerprints from living tissues without requiring sample preparation. DART-MS measurement of skin tissue with multivariate analysis proved to be a powerful method to discriminate between experimental groups and to find biomarkers for various experiment models in skin dermatological research.