This study investigated the applicability of a metabolomics approach based on ultra-performance liquid chromatography to quadrupole time-of-flight spectrometry (UPLC-QTOF/MS) combined with multivariate statistical analysis for the discrimination of gamma-irradiated soybeans. Domestically produced, non-irradiated organic soybeans were used as the control, while soybeans irradiated at absorbed doses of 1, 3, and 5 kGy were used as the experimental groups. A total of 51 metabolites were identified through UPLC-QTOF/MS analysis of soybean extracts. Partial least squares-discriminant analysis (PLS-DA) revealed that the metabolic profiles of non-irradiated and irradiated soybeans were significantly separated in both positive and negative ion modes (p<0.05). To identify the metabolites contributing to group discrimination, compounds satisfying both statistical significance (p<0.05) and variable importance in projection (VIP>0.5). Among these, histidine, fumaric acid, malonic acid, uric acid, adenosine, cis-aconitic acid, xanthine, dihydrodaidzein, genistein, kaempferol, and soyasaponin Be showed significant dose-dependent differences. They were therefore proposed as potential biomarkers for discrimination gamma-irradiated soybeans. These results indicate that UPLC-QTOF/MS-based metabolomic profiling combined with multivariate analysis is an effective analytical tool for the identification and authentication of gamma-irradiated soybeans.

One of the leading pests of rice, small brown planthopper (Laodelphax striatellus) can grow up to have either short or long wings, depending on conditions. However, under the same breeding conditions, the phenotypes of the long- and short-winged small brown planthopper observed to keep the first collected phenotype. To investigate the mechanism involved in wing dimorphism, metabolomic researches have been conducted. In this study, we observed several metabolites change, and the difference of metabolites could provide clues to the relationship between physiological changes in the small brown planthopper and ecological transport.

Polyphenism is one of the most intriguing phenomenon observed from various insect species. Polyphenism is changing morphological, behavioral, and physiological phenotypes without changing genome informations. Therefore, polyphenism is thought to be controlled by epigenetic methods. Epigenetic controls are including DNA methylation, histone acetylation, small RNAs, long non-coding RNAs, and protein phosphorylation. To investigate the mechanism involved in polyphenism, various genomic researches have been completed. However, metabolite changes in different phases have not been reported yet. Thus, we studied the metabolite changes and found the changed metabolites. These studies will make an important contribution to reveal the difference between gregrious and solitary phases and reveal eco - friendly control techniques.

배양시간(6, 12, 24, 48시간)에 따른 Yersinia enterocolitica 균체 및 배지 대사체들의 변화를 이해하기 위하여 GC/MS와 다변량통계분석을 이용하여 분석한 결과 Partial Least Squares-Discriminant Analysis(PLS-DA) scores plots 상에서 배양시간에 따라 분석 시료들이 뚜렷하게 분리되었다. 시료들 사이의 차이에 관여하는 대사물질들을 동정한 결과 균체에서는 ethylene glycol, valine, ethanolamine, succinic acid, adenine, stearic acid, 배지에서는 glycine, fumaric acid, threonine, aminomalonic acid, malic acid, glutamic acid, citric acid가 배양시간에 따른 차이가 나는 것으로 확인되었다. 이들 대사물질들을 이용하여 관련 대사경로를 도출한 결과 배양시기에 따라 에너지 생성에 관여하는 대사경로가 주로 변하는 것으로 관찰되었다. Y. enterocolitica의 경우 대수기 초기에는 TCA cycle을 통해 에너지를 공급하다가 정지기에 들어서면서 정상적인 TCA cycle를 수행하지 못하고 ethanolamine 및 ethylene glycol 등 다른 탄소원 또는 질소원을 공급하는 것으로 확인되었다. 비록 관련된 많은 연구가 필요함에도 불구하고 대사체분석 기술을 활용한 Y. enterocolitica 균체 및 배지 분석 연구는 배양시간에 따른 대사과정의 변화를 관찰할 수 있을 뿐만 아니라 향후 균체 연구의 다양한 분야에 접목될 수 있을 것으로 사료된다.

Background : Wild-simulated ginseng (WSG, Panax ginseng C. A. Meyer) in Korea which depends on an artificial forest growth method. Methods and Results : WSG samples were collected from 8 different regions in Korea in October, 2017, and these were analyzed by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS) using optimized analytical methods to compare the metabolite patterns to cultivated regions. Furthermore, their metabolite compositions differed according to individual plant samples. The metabolite profiling data were processed by multi-variate statistical analyses such as PCA and PLS-DA to determine the differences among geographical origins. To identify significant metabolites according to regional variances of WSG, more sophisticated multi-variate statistical analyses such as metabolite selection should be performed. Conclusion : This metabolomics approach can also be applied to evaluate the overall quality of WSG, as well as to discriminate the cultivars for the medicinal plant industry.