Seoul National University · Medicine
Professor Minsoo Noh's research lab specializes in translational biomedical research, focusing on molecular mechanisms underlying skin diseases, stem cell biology, and metabolic disorders. The lab integrates systems biology approaches—such as meta-analysis of high-throughput omics data—with functional validation in primary human cells to identify key regulatory genes and pathways. A central theme is the identification of novel therapeutic targets in keratinocyte differentiation, adiponectin regulation, and nuclear receptor signaling (e.g., PPARγ/δ), with applications in inflammatory skin diseases and metabolic syndrome. The lab also pioneers innovative models for studying rare cell populations, such as distributed stem cells, using genetically engineered systems to uncover asymmetric self-renewal signatures.
Figures are computed from collected data and may differ slightly.
In the meta-analysis of public microarray databases for different skin diseases, we revealed seven commonly up-regulated genes, DSG3, KRT6, MAP17, PLSCR1, RPM2, SOD2 and SPRR2B. We postulated that the genes selected from the meta-analysis may be potentially associated with the abnormal keratinocyte differentiation. To demonstrate this postulation, we alternatively evaluated whether the genes of interest in the meta-analysis can be regulated by T-helper (Th) cell cytokines in normal human epiderm
The therapeutic potential of adiponectin regulation has received interest because of its association with diverse human disease conditions, such as diabetes, obesity, atherosclerosis, and cancer. Phenylethylchromone derivatives from Aquilaria malaccensis-derived agarwood promoted adiponectin secretion during adipogenesis in human bone marrow mesenchymal stem cells, and 5,6-dihydroxy-2-(2-phenylethyl)chromone (1) was identified as a new chromone derivative. A target identification study with the
In biometrical genetic analyses of binary traits, the use of family data overcomes some limitations of twin studies, particularly in terms of sample size and types of genetic or environmental factors that can be estimated. However, because of computational problems, recent methods in the application of generalized linear mixed models for family data structure have limited the ability to handle large data sets with general covariates. In this paper, we investigate the use of the hierarchical like
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