Korea University · 農学・生物学
Professor Hong-Seok Son's research lab specializes in microbial and metabolomic analysis, focusing on the interplay between gut and environmental microbiota and their metabolic profiles in relation to human health and food fermentation. The lab employs advanced omics technologies—such as 1H NMR, GC-MS, and 16S rRNA sequencing—to investigate microbial communities and metabolites in fermented foods (e.g., kimchi, wine), host-microbe interactions (e.g., in obesity), and oral health (e.g., halitosis). A central theme is understanding how microbial metabolites influence physiological outcomes and food quality across different environmental and dietary conditions. The lab also applies machine learning to metabolomic data for predictive modeling of biological states, such as ripening stages or disease-related shifts.
Figures are computed from collected data and may differ slightly.
(1)H NMR spectroscopy was used to investigate the metabolic differences in wines produced from different grape varieties and different regions. A significant separation among wines from Campbell Early, Cabernet Sauvignon, and Shiraz grapes was observed using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The metabolites contributing to the separation were assigned to be 2,3-butanediol, lactate, acetate, proline, succinate, malate, glycerol, tartarate
Obesity can be caused by microbes producing metabolites; it is thus important to determine the correlation between gut microbes and metabolites. This study aimed to identify gut microbiota-metabolomic signatures that change with a high-fat diet and understand the underlying mechanisms. To investigate the profiles of the gut microbiota and metabolites that changed after a 60% fat diet for 8 weeks, 16S rRNA gene amplicon sequencing and gas chromatography-mass spectrometry (GC-MS)-based metabolomic
Kimchi is a traditional fermented vegetable side dish in Korea and has become a global health food. Kimchi undergoes spontaneous fermentation, mainly by lactic acid bacteria (LAB) originating from its raw ingredients. Numerous LAB, including the genera <i>Leuconostoc</i>, <i>Weissella</i>, and <i>Lactobacillus</i>, participate in kimchi fermentation, reaching approximately 9-10 log colony forming units per gram or milliliter of food. The several health benefits of LAB (e.g., antioxidant and anti
Halitosis is mainly caused by the action of oral microbes. The purpose of this study was to investigate the differences in salivary microbes and metabolites between subjects with and without halitosis. Of the 52 participants, 22 were classified into the halitosis group by the volatile sulfur compound analysis on breath samples. The 16S rRNA gene amplicon sequencing and metabolomics approaches were used to investigate the difference in microbes and metabolites in saliva of the control and halitos
본 연구에서는 로즈마리의 항산화 물질을 최대로 추출할 수 있는 최적의 추출 조건을 확립하였다. 열수 추출의 경우에는 <TEX>$90^{\circ}C$</TEX>, 30분 추출할 경우 항산화력(<TEX>$IC_{50}$</TEX> 값이 4.49 g DM/L)과 총 페놀함량(28.30 mg GAE/g DM)이 다른 온도와 시간보다 높은 수치를 나타내었다. 에탄올 추출의 경우에는 50%에탄올, <TEX>$70^{\circ}C$</TEX>, 10분의 경우가 항산화력(<TEX>$IC_{50}$</TEX> 값이 2.60 g DM/L), 총 페놀함량(40.28 mg GAE/g DM)이 다른 농도와 온도, 시간보다 높은 결과를 나타내었다 메탄올 추출 결과는 75% 메탄올, <TEX>$60^{\circ}C$</TEX>, 30분의 경우가 항산화력(<TEX>$IC_{50}$</TEX> 값이 2.31 g DM/L), 총 페놀함량(57.22 GAE/g DM)이 다른 농도와 온도, 시간보다 높은 수치를 나타
In fermented foods, including kimchi, salinity is a crucial factor that influences preservation and quality. We aimed to investigate the physicochemical characteristics, microbiota, and metabolites of kimchi fermented at varying salinities over 200 days. Kimchi cabbages were soaked in 3%, 10%, and 20% (w/v) saline solutions for 10 h to prepare low-, middle-, and high-salinity kimchi, respectively. Although the salinity level of kimchi had a significant impact on its appearance, microbial communi
The purpose of this study was to analyze metabolic differences of ginseng berries according to cultivation age and ripening stage using gas chromatography-mass spectrometry (GC-MS)-based metabolomics method. Ginseng berries were harvested every week during five different ripening stages of three-year-old and four-year-old ginseng. Using identified metabolites, a random forest machine learning approach was applied to obtain predictive models for the classification of cultivation age or ripening s
These results suggest that a metabolomics approach based on GC-MS can be a useful tool to understand ginseng fermentation and evaluate the fermentative characteristics of starter cultures.
Accumulated clinical and biomedical evidence indicates that the gut microbiota and their metabolites affect brain function and behavior in various central nervous system disorders. This study was performed to investigate the changes in brain metabolites and composition of the fecal microbial community following injection of amyloid β (Aβ) and donepezil treatment of Aβ-injected mice using metataxonomics and metabolomics. Aβ treatment caused cognitive dysfunction, while donepezil resulted in the s
Age-related gut microbes and urine metabolites were investigated in 568 healthy individuals using metataxonomics and metabolomics. The richness and evenness of the fecal microbiota significantly increased with age, and the abundance of 16 genera differed between the young and old groups. Additionally, 17 urine metabolites contributed to the differences between the young and old groups. Among the microbes that differed by age, <i>Bacteroides</i> and <i>Prevotella</i> 9 were confirmed to be correl
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