노민수 교수
Min-Sun Oh
서울대학교 의약학과 · 의학
연구실 소개
노민수 교수의 연구실은 피부질환의 분자 기전을 밝히고, 특히 편평상피세포의 비정상적 분화와 연관된 유전자 발현 조절 메커니즘을 중심으로 연구를 진행하고 있습니다. 또한, 대사질환과 암 등과 연관된 어드ิ포넥틴 조절 메커니즘과 그 신호전달 경로를 규명하며, 유전자 발현 조절, 간엽줄기세포의 대사 조절, 신경질환 모델에서의 약물 행동학적 영향까지 다각도로 연구를 확장하고 있습니다. 특히, 유전자 마이크로어레이 및 생물정보학적 분석을 기반으로 한 표적 유전자 식별과 기능 검증이 핵심 기반 기술입니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15In 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
A 3 adenosine receptor (AR) ligands including A 3 AR agonist, N 6 -(3-iodobenzyl)adenosine-5′- N -methyluronamide ( 1a, IB-MECA) were examined for adiponectin production in human bone marrow mesenchymal stem cells (hBM-MSCs). In this model, 1a significantly increased adiponectin production, which is associated with improved insulin sensitivity. However, A 3 AR antagonists also promoted adiponectin production in hBM-MSCs, indicating that the A 3 AR pathway may not be directly involved in the adip
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
대표 연구 분야
노민수 교수의 연구를 Nubint에서 더 깊이 살펴보세요
이 연구실의 논문을 앱에서 열어 AI와 함께 읽고, 핵심을 요약하고, 내 글에 인용하세요.