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Regulation of Acetyl-CoA Carboxylase Gene Expressionby Hormones and Nutrients
김양하,김윤정,양정례,권인숙 한국식품영양과학회 2003 Preventive Nutrition and Food Science Vol.8 No.1
This study was investigated to identify the regulatory mechanism of ACC gene expression by hormones and nutrition. The fragment of ACC promoter I (PI) -220 bp region was recombined to pGL3-Basic vector with luciferase as a reporter gene. The primary hepatocyte from the rat was used to investigate the regulation of ACC PI activity. ACC PI (-220 bp)/luciferase chimeric plasmid was transfected into primary rat hepatocyte by using lipofectin. ACC PI activity was shown by measuring luciferase activity. The addition of insulin, dexamethasone, and triiodothyronine to the culture medium increased the activity of ACC PI by 2.5-, 2.3- and 1.8-fold, respectively. In the presence of 1 M dexamethasone, the effects of insulin was amplified about 1.2-fold showing the additional effects of dexamethasone. Moreover the activity of luciferase was increased by insulin, dexamethasone, and triiodothyronine treatment approximately 4-fold. These results indicated that insulin, dexamethasone and thyroid hormone coordinately regulate ACC gene expression via regulation of promoter I activity. On the -220 to +21 region of ACC PI, the addition of the glucose to the culture medium increased the activity of ACC PI. With 25 mM glucose, luciferase activity increased by 7-fold. On the other hand, on the -220 bp region, ACC PI activity was not changed by polyunsaturated fatty acids. Therefore, it can be postulated that there are response elements for insulin, triiodothyronine, dexamethasone, and glucose, but not PUFAs on the -220 bp region of ACC PI. This study was investigated to identify the regulatory mechanism of ACC gene expression by hormones and nutrition. The fragment of ACC promoter I (PI) -220 bp region was recombined to pGL3-Basic vector with luciferase as a reporter gene. The primary hepatocyte from the rat was used to investigate the regulation of ACC PI activity. ACC PI (-220 bp)/luciferase chimeric plasmid was transfected into primary rat hepatocyte by using lipofectin. ACC PI activity was shown by measuring luciferase activity. The addition of insulin, dexamethasone, and triiodothyronine to the culture medium increased the activity of ACC PI by 2.5-, 2.3- and 1.8-fold, respectively. In the presence of 1 μM dexamethasone, the effects of insulin was amplified about 1.2-fold showing the additional effects of dexamethasone. Moreover the activity of luciferase was increased by insulin, dexamethasone, and triiodothyronine treatment approximately 4-fold. These results indicated that insulin, dexamethasone and thyroid hormone coordinately regulate ACC gene expression via regulation of promoter I activity. On the -220 to +21 region of ACC PI, the addition of the glucose to the culture medium increased the activity of ACC PI. With 25 mM glucose, luciferase activity increased by 7-fold. On the other hand, on the -220 bp region, ACC PI activity was not changed by polyunsaturated fatty acids. Therefore, it can be postulated that there are response elements for insulin, triiodothyronine, dexamethasone, and glucose, but not PUFAs on the -220 bp region of ACC PI.
당뇨병연구를 위한 유전학적 접근 : 형질전환 마우스 모델
김양하 한국식품영양과학회 1999 식품산업과 영양 Vol.4 No.3
Non-insulin-dependent diabetes mellitus (NIDDM) is characterized by insulin resistance and impaired insulim secretion. The transgenic technology, in which a specific gene can be introduced or deleted to study its function, has been established. A number of transgenic mice, altered the expression of genes potentially involved in insulin action or pancreatic ${\beta}$-cell function, have recently been developed to address questions concerning NIDDM. Thransgenic mice model may help understanding the molecular basis of complex patho-physiologies of NIDDM. This review outlines the new insights obtained from the studies of transgenic mice that overxpress or show decreased expression of putative key genes involved in the regulation of insulin resistance and pancreatic ${\beta}$-cell function, therefore in the control of glucose homeostasis.
화장의 자의식적 특성 분석과 화장마음의 사회인지모형 검증
김양하,김기범,차영란 한국여성심리학회 2007 한국심리학회지 여성 Vol.12 No.2
The purpose of this study was to test the socio-cognitive model of make-up maum of Korean women. In order to build make-up maum model, we approached meaning of make-up, reason for make-up, positive and negative function of make-up by qualitative method. Then we performed content analysis of qualitative data. We also collected quantitative data from Korean university students and adults. A total of 335 respondents was participated in this study. We tested the socio-cognitve model for make-up maum by strutural equation modeling. The results indicated that desire for make-up and make-up belief as a category of maum influenced positively on make-up behavior. However, this model was suitable for only adults sample. In addition, we tested the effect of emotion on make-up maum cosisted of desire and belief. The results showed that felt emotion during make-up related positively to desire and belief repectively. Also, emotion influenced indirectly on make-up behavior.
김양하 한국영양학회 2022 Journal of Nutrition and Health Vol.55 No.1
In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.