• 한국약용작물학회:학술대회논문집
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• 2008.11a
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• pp.321-322
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• 2008

• 한국약용작물학회:학술대회논문집
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• 2008.05a
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• pp.162-163
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• 2008

• Journal of Microbiology and Biotechnology
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• v.22 no.1
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• pp.100-106
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• 2012

In this study, the metabolite profiles of three Aspergillus strains during rice koji fermentation were compared. In the partial least squares discriminant analysis-based gas chromatography-mass spectrometry data sets, the metabolite patterns of A. oryzae (KCCM 60345) were clearly distinguished from A. kawachii (KCCM 60552) and only marginal differences were observed for A. oryzae (KCCM 60551) fermentation. In the 2 days fermentation samples, the overall metabolite levels of A. oryzae (KCCM 60345) were similar to the A. oryzae (KCCM 60551) levels and lower than the A. kawachii (KCCM 60552) levels. In addition, we identified discriminators that were mainly contributing tyrosinase inhibition (kojic acid) and antioxidant activities (pyranonigrin A) in A. oryzae (KCCM 60345) and A. kawachii (KCCM 60552) inoculated rice koji, respectively. In this study, we demonstrated that the optimal inoculant Aspergillus strains and fermentation time for functional rice koji could be determined through a metabolomics approach with bioactivity correlations.

• Journal of Microbiology and Biotechnology
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• v.21 no.1
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• pp.5-13
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• 2011

In this study, seven Trichoderma species (33 strains) were classified using secondary metabolite profile-based chemotaxonomy. Secondary metabolites were analyzed by liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS-MS) and multivariate statistical methods. T. longibrachiatum and T. virens were independently clustered based on both internal transcribed spacer (ITS) sequence and secondary metabolite analyses. T. harzianum formed three subclusters in the ITS-based phylogenetic tree and two subclusters in the metabolitebased dendrogram. In contrast, T. koningii and T. atroviride strains were mixed in one cluster in the phylogenetic tree, whereas T. koningii was grouped in a different subcluster from T. atroviride and T. hamatum in the chemotaxonomic tree. Partial least-squares discriminant analysis (PLS-DA) was applied to determine which metabolites were responsible for the clustering patterns observed for the different Trichoderma strains. The metabolites were hetelidic acid, sorbicillinol, trichodermanone C, giocladic acid, bisorbicillinol, and three unidentified compounds in the comparison of T. virens and T. longibrachiatum; harzianic acid, demethylharzianic acid, homoharzianic acid, and three unidentified compounds in T. harzianum I and II; and koninginin B, E, and D, and six unidentified compounds in T. koningii and T. atroviride. The results of this study demonstrate that secondary metabolite profiling-based chemotaxonomy has distinct advantages relative to ITS-based classification, since it identified new Trichoderma clusters that were not found using the latter approach.

• Bulletin of the Korean Chemical Society
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• v.26 no.5
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• pp.729-734
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• 2005

A sensitive and selective method for quantitation of sofalcone and its active metabolite in human plasma has been established using liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI/MS/MS). Plasma samples were transferred into 96-well plate using an automated sample handling system and spiked with 10 $\mu$L of 2 $\mu$g/mL $d_3$-sofalcone and $d_3$-sofalcone metabolite solutions (internal standard), respectively. After adding 0.5 mL of acetonitrile to the 96-well plate, the plasma samples were then vortexed for 30 sec. After centrifugation, the supernatant was transferred into another 96-well plate and completely evaporated at 40 ${^{\circ}C}$ under a stream of nitrogen. Dry residues were reconstituted with mobile phase and were injected into a $C_{18}$ reversed-phase column. The limit of quantitation of sofalcone and its metabolite was 2 ng/mL, using a sample volume of 0.2 mL for analysis. The reproducibility of the method was evaluated by analyzing 10 replicates over the concentration range of 2 ng/mL to 1000 ng/mL. The validation experiments of the method have shown that the assay has good precision and accuracy. Sofalcone and its metabolite produced a protonated precursor ion ([M+H]$^+$) of m/z 451 and 453, and a corresponding product ion of m/z 315 and 317, respectively. Internal standard ($d_3$-sofalcone and $d_3$-sofalcone metabolite) produced a protonated precursor ion ([M+H]$^+$) of m/z 454 and 456 and a corresponding product ion of m/z 315 and 317, respectively. The method has been successfully applied to a pharmacokinetic study of sofalcone and its active metabolite in human plasma.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.1
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• pp.79-90
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• 2020

Objective: In the present study, an liquid chromatography/mass spectrometry (LC/MS) metabolomics approach was performed to investigate potential biomarkers of milk production in high- and low-milk-yield dairy cows and to establish correlations among rumen fluid metabolites. Methods: Sixteen lactating dairy cows with similar parity and days in milk were divided into high-yield (HY) and low-yield (LY) groups based on milk yield. On day 21, rumen fluid metabolites were quantified applying LC/MS. Results: The principal component analysis and orthogonal correction partial least squares discriminant analysis showed significantly separated clusters of the ruminal metabolite profiles of HY and LY groups. Compared with HY group, a total of 24 ruminal metabolites were significantly greater in LY group, such as 3-hydroxyanthranilic acid, carboxylic acids, carboxylic acid derivatives (L-isoleucine, L-valine, L-tyrosine, etc.), diazines (uracil, thymine, cytosine), and palmitic acid, while the concentrations of 30 metabolites were dramatically decreased in LY group compared to HY group, included gentisic acid, caprylic acid, and myristic acid. The metabolite enrichment analysis indicated that protein digestion and absorption, ABC transporters and unsaturated fatty acid biosynthesis were significantly different between the two groups. Correlation analysis between the ruminal microbiome and metabolites revealed that certain typical metabolites were exceedingly associated with definite ruminal bacteria; Firmicutes, Actinobacteria, and Synergistetes phyla were highly correlated with most metabolites. Conclusion: These findings revealed that the ruminal metabolite profiles were significantly different between HY and LY groups, and these results may provide novel insights to evaluate biomarkers for a better feed digestion and may reveal the potential mechanism underlying the difference in milk yield in dairy cows.

• Journal of Microbiology and Biotechnology
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• v.20 no.4
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• pp.809-816
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• 2010

Microbial biotransformations can be used to predict mammalian drug metabolism. The present investigation deals with microbial biotransformation of valdecoxib using microbial cultures. Thirty-nine bacterial, fungal, and yeast cultures were used to elucidate the biotransformation pathway of valdecoxib. A number of microorganisms metabolized valdecoxib to various levels to yield nine metabolites, which were identified by HPLC-DAD and LC-MS-MS analyses. HPLC analysis of biotransformed products indicated that a majority of the metabolites are more polar than the substrate valdecoxib. Basing on LC-MS-MS analysis, the major metabolite was identified as a hydroxymethyl metabolite of valdecoxib, whereas the remaining metabolites were produced by carboxylation, demethylation, ring hydroxylation, N-acetylation, or a combination of these reactions. The hydroxymethyl and carboxylic acid metabolites were known to be produced in metabolism by mammals. From the results, it can be concluded that microbial cultures, particularly fungi, can be used to predict mammalian drug metabolism.

• 한국약용작물학회:학술대회논문집
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• 2008.05a
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• pp.158-159
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• 2008

• Journal of Environmental Health Sciences
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• v.44 no.5
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• pp.433-443
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• 2018

Objectives: The purpose of this study was to examine the relationship between dietary habits and concentrations of urinary phthalate metabolite in elementary school children. Methods: This study was conducted in Seoul, South Korea. We collected urine samples from 156 children from a single school in September 2016. Information on dietary habits was obtained from the parents of the children. Five types of urinary phthalate metabolites were analyzed using a high-performance liquid chromatography tandem mass spectrometer. Multiple linear regression analysis was used to determine the factors affecting the concentrations of urinary phthalate metabolite. Results: Girls had a significantly lower concentration of urinary mono-n-butyl phthalate (MnBP) (p=0.006) than did boys. When the consumption of bean processed food and wrapped delivery food were more than once a week, urinary MnBP (p=0.021) and monobenzyl phthalate (MBzP) (p=0.032) concentrations were the highest, respectively. Conclusion: Several urinary phthalate metabolite concentrations were associated with demographic and dietary habits. Particularly, urinary MnBP and MBzP were associated with several processed foods. The findings of this study might be useful as basic data for establishing and educating on environmental health policy.

• Mass Spectrometry Letters
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• v.11 no.1
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• pp.1-5
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• 2020

In this study, some of the recently reported data processing strategies were evaluated and modified based on their capabilities and a brief workflow for data mining was redefined for Q-TOF LC-MS based untargeted metabolomics. Commercial pooled human plasma samples were used for this purpose. An ultrafiltration procedure was applied on sample preparation. Sample set was analyzed through Q-TOF LC/MS. A C18 column (Agilent Zorbax 1.8 µM, 50 × 2.1 mm) was used for chromatographic separation. Raw chromatograms were processed using XCMS - R programming language edition and Isotopologue Parameter Optimization (IPO) was used to optimize XCMS parameters. The raw XCMS table was processed using MS Excel to find reliable and reproducible peaks. Totally 1650 reliable and reproducible potential metabolite peaks were found based on the data processing procedures given in this paper. The redefined dataset was upload into MetaboAnalyst platform and the identified metabolites were matched with 86 metabolic pathways. Thus, two list were obtained and presented in this study as supplement files. The first list is to present the retention times and m/z values of detected metabolite peaks. The second list is the metabolic pathways related with the identified metabolites. The briefly described data processing strategies and dataset presented in this study could be beneficial for the researchers working on untargeted metabolomics for processing their data and validating their results.