• Journal of Microbiology and Biotechnology
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• 제27권6호
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• pp.1157-1162
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• 2017

The goal of this study was to identify and characterize selected Trichoderma isolates by metabolic profiling and enzyme assay for evaluation of their potential as biocontrol agents against plant pathogens. Trichoderma isolates were obtained from the Rural Development Administration Genebank Information Center (Wanju, Republic of Korea). Eleven Trichoderma isolates were re-identified using ribosomal DNA internal transcribed spacer (ITS) regions. ITS sequence results showed new identification of Trichoderma isolates. In addition, metabolic profiling of the ethyl acetate extracts of the liquid cultures of five Trichoderma isolates that showed the best anti-Phytophthora activities was conducted using gas chromatography-mass spectrometry. Metabolic profiling revealed that Trichoderma isolates shared common metabolites with well-known antifungal activities. Enzyme assays indicated strong cell wall-degrading enzyme activities of Trichoderma isolates. Overall, our results indicated that the selected Trichoderma isolates have great potential for use as biocontrol agents against plant pathogens.

• 한국응용약물학회:학술대회논문집
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• 한국응용약물학회 2007년도 Proceedings of The Convention
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• pp.19-27
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• 2007

An important potential of metabolomics-based approach is the possibility to develop fingerprints of diseases or cellular responses to classes of compounds with known common biological effect. Such fingerprints have the potential to allow classification of disease states or compounds, to provide mechanistic information on cellular perturbations and pathways and to identify biomarkers specific for disease severity and drug efficacy. Metabolic profiles of biological fluids contain a vast array of endogenous metabolites. Changes in those profiles resulting from perturbations of the system can be observed using analytical techniques, such as NMR and MS. $^1H$ NMR was used to generate a molecular fingerprint of serum or urinary sample, and then pattern recognition technique was applied to identity molecular signatures associated with the specific diseases or drug efficiency. Several metabolites that differentiate disease samples from the control were thoroughly characterized by NMR spectroscopy. We investigated the metabolic changes in human normal and clinical samples using $^1H$ NMR. Spectral data were applied to targeted profiling and spectral binning method, and then multivariate statistical data analysis (MVDA) was used to examine in detail the modulation of small molecule candidate biomarkers. We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences between healthy and disease population. Such metabolic signatures could provide diagnostic markers for a disease state or biomarkers for drug response phenotypes.

• Journal of Plant Biotechnology
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• 제33권3호
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• pp.161-169
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• 2006

식물대사체학은 식물세포, 조직, 기관, 혹은 개체수준에서 주어진 시간과 조건에서 발견되는 모든 대사물질을 밝히고, 시간의 경과 혹은 조건의 변화에 따른 metabolic profiling의 변화를 연구하는 식물학 분야이다. 식물대사체학은 생물에 대한 전체론적 접근을 위한 가장 최근에 발달된 omics분야의 하나로서 일종의 시스템 생물학이다. 전체론적 접근과 이해를 위해서 대사체학은 metabolic profiling의 계량화학 혹은 다변량분석 방법을 자주 사용한다. 식물학분야에서 대사체학은 애기장대나 벼와 같은 모델식물에 tag를 도입하여 형질전환시킨 돌연변이체에 대해 DNA microarray와 함께 사용하여 유전자의 기능을 밝히는데 유용하게 사용된다. 본 총설에서는 식물대사체학의 기본 개념과 1H NMR 혹은 FTIR으로 얻은 metabolic profiling의 다변량분석에 대한 실용적인 사용법을 소개하고자 하였다.

• Bulletin of the Korean Chemical Society
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• 제34권5호
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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.

• Mass Spectrometry Letters
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• 제5권2호
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• pp.35-41
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• 2014

Gas chromatography-mass spectrometry (GC-MS) methods have been used extensively in clinical steroid analyses. Evaluating the metabolic ratios of precursors to products by accurate quantification of individual steroid levels in biological samples can reveal the activities of enzymes associated with steroid metabolism. This review article discusses the impact of GC-MS-based steroid profiling on our understanding of the biochemical role of steroids and their metabolic enzymes in hormone-dependent diseases, such as congenital adrenal hyperplasia (CAH), cortisol-mediated hypertension, apparent mineralocorticoid excess (AME), male-pattern baldness, and breast and thyroid cancers. Steroid profiling is a comprehensive analytical technique that can be applied whenever the highest specificity is required and may be a reasonable initial diagnostic approach.

• 한국자기공명학회논문지
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• 제15권1호
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• pp.54-68
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• 2011

$^1H$ nuclear magnetic resonance (NMR) spectroscopy of biological samples has been proven to be an effective and nondestructive approach to probe drug toxicity within an organism. In this study, ketoprofen toxicity was investigated using $^1H$-NMR spectroscopy coupled with multivariate statistical analysis. Histopathologic test of ketoprofen-induced acute gastrointestinal damage in rats demonstrated a significant dose-dependent effect. Furthermore, principal component analysis (PCA) derived from $^1H$-NMR spectra of urinary samples showed clear separation between the vehicle-treated control and ketoprofen-treated groups. Moreover, PCA derived from endogenous metabolite concentrations through targeted profiling revealed a dose-dependent metabolic shift between the vehicle-treated control, low-dose ketoprofen-treated (10 mg/kg body weight), and high-dose ketoprofen-treated (50 mg/kg) groups coinciding with their gastric damage scores after ketoprofen administration. The resultant metabolic profiles demonstrated that the ketoprofen-induced gastric damage exhibited energy metabolism perturbations that increased urinary levels of citrate, cis-aconitate, succinate, and phosphocreatine. In addition, ketoprofen administration induced an enhancement of xenobiotic activity in fatty oxidation, which caused increase levels of N-isovalerylglycine, adipate, phenylacetylglycine, dimethylamine, betaine, hippurate, 3-indoxylsulfate, N,N-dimethylglycine, trimethyl-N-oxide, and glycine. These findings demonstrate that $^1H$-NMR-based urinary metabolic profiling can be used for noninvasive and rapid way to diagnose adverse drug effects and is suitable for explaining the possible biological pathways perturbed by nonsteroidal anti-inflammatory drug toxicity.

• Mycobiology
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• 제50권2호
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• pp.110-120
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• 2022

The goal of the present study was to investigate the antibacterial properties, enzyme production, and metabolic profiling of a new Ceratorhiza hydrophila strain isolated from the submerged aquatic plant Myriophyllum spicatum. Furthermore, the fungus' morphological characterization and DNA sequencing have been described. The fungus has been identified and submitted to the GenBank as Ceratorhiza hydrophila isolate EG19 and the fungus ID is MK387081. The enzyme analyses showed its ability to produce protease and cellulase enzymes. According to the CSLI standard, the ethyl acetate extract of C. hydrophila showed intermediate antibacterial activity against Streptococcus pneumonia, Micrococcus luteus, and Staphylococcus aureus. Metabolic profiling has been carried out using 700 MHz NMR spectroscopy. Based on the ¹H and ¹H-¹³C heteronuclear single quantum coherence (HSQC) NMR data and NMR databases, 23 compounds have been identified. The identified metabolites include 31% amino acids, 9% sugars, 9% amines, 4% sugar alcohols, and 4% alkaloids. This is the first report for the metabolic characterization of C. hydrophila, which gave preliminary information about the fungus. It is expected that our findings not only will pave the way to other perspectives in enormous applications using C. hydrophila as a new promising source of antimicrobial agents and essential metabolites, but also it will be valuable in the classification and chemotaxonomy of the species.

• 한국자기공명학회논문지
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• 제22권1호
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• pp.10-17
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• 2018

Metabolite identification and quantification are one of the foremost important issues in metabolomics. In NMR based metabolomics, mainly one-dimensional proton NMR spectra of biofluids, such as urine and serum are measured. However, it is not always easy to identify and quantify metabolites in one-dimensional proton NMR spectra. This article introduces useful public metabolite databases, metabolic profiling software, and articles.

• 대한유전성대사질환학회지
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• 제6권1호
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• pp.40-51
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• 2006

Purpose: A personal computer-based system was developed for automated metabolic profiling of organic aciduria and aminoacidopathy by gas chromatography-mass spectrometry and data interpretation for the diagnosis of metabolic disorders Methods: For automatic data profiling and interpretation, we compiled retention time, two target ions and their intensity ratio for 77 organic acids and 13 amino acids metabolites. Metabolites above the cut-off values were flagged as abnormal compounds. The data interpretation was a based on combination of flagged metabolites. Diagnostic or index metabolites were categorized into three groups, "and", "or" and "NO" compiled for each disorder to improve the specificity of the diagnosis. Groups "and" and "or" comprised essential and optional compounds, respectively, to reach a specific diagnosis. Group "NO" comprised metabolites that must be absent to make a definite diagnosis. We tested this system by analyzing patients with confirmed Propionic aciduria and others. Results: In all cases, the diagnostic metabolites were identified and correct diagnosis was founded to be made among the possible disease suggested by the system. Conclusion: The study showed that the developed method could be the method of choices in rapid, sensitive and simultaneous screening for organic aciduria and amino acidopathy with this simplified automated system.

• Journal of Ginseng Research
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• 제36권3호
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• pp.277-290
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• 2012

The metabolic profiles of Panax quinquefolius and its associated therapeutic values are critically affected by the repetitious steaming times. The times-dependent steaming effect of P. quinquefolius is not well-characterized and there is also no official guideline on its times of steaming. In this paper, a UPLC-Q-TOF-MS/MS method was developed for the qualitative profiling of multi-parametric metabolic changes of raw P. quinquefolius during the repetitious steaming process. Our method was successful in discriminating the differentially multi-steamed herbs. Meantime, the repetitious steaming-inducing chemical transformations in the preparation of black American ginseng (American ginseng that was subjected to 9 cycles of steaming treatment) were evaluated by this UPLC-Q-TOF-MS/MS based chemical profiling method. Under the optimized UPLC-Q-TOF-MS/MS conditions, 29 major ginsenosides were unambiguously identified and/or tentatively assigned in both raw and multi-steamed P. quinquefolius within 19 min, among them 18 ginsenosides were detected to be newly generated during the preparatory process of black American ginseng. The mechanisms involved were further deduced to be hydrolysis, dehydration, decarboxylation and addition reactions of the original ginsenosides in raw P. quinquefolius through analyzing mimic 9 cycles of steaming extracts of 14 pure reference ginsenosides. Our novel steaming times-dependent metabolic profiling approach represents the paradigm shift in the global quality control of multi-steamed P. quinquefolius products.