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    ๋ฐฑ๋…„์ดˆ(Opuntia ficus-indica) ์—ด๋งค ์œ ๋ž˜ ๋ฌผ์งˆ์˜ ํ•ญ๊ถค์–‘ ํšจ๊ณผ, ๋…์„ฑ ๋ฐ ์ž‘์šฉ ๊ธฐ์ „

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋†์—…์ƒ๋ฌผ๊ณตํ•™์ „๊ณต, 2021. 2. ์–‘ํƒœ์ง„.์œ„์—ผ์€ ์ฃผ๋กœ ์ง  ์Œ์‹๊ณผ ๋งค์šด ์Œ์‹์„ ์ฃผ๋กœ ๋จน๋Š” ํ•œ๊ตญ ์„ฑ์ธ๋“ค์—๊ฒŒ ํ”ํ•œ ์งˆ๋ณ‘์ด ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ์ฆ์ƒ์—๋Š” ์ƒ๋ณต๋ถ€ ํ†ต์ฆ, ๋ฉ”์Šค๊บผ์›€, ๊ตฌํ† , ๋ณตํ†ต, ์†Œํ™” ๋ถˆ๋Ÿ‰ ๋ฐ ๋ณต๋ถ€ ํŒฝ ๋งŒ๊ฐ์ด ์žˆ๋‹ค. ์œ„์—ผ์€ ๋ณ‘ํƒœ์ƒ๋ฆฌํ•™์ ์œผ๋กœ ์œ„ ๊ณต๊ฒฉ ์ธ์ž (์‚ฐ, ํŽฉ์‹ , ํ—ฌ๋ฆฌ์ฝ”๋ฐ•ํ„ฐ ํŒŒ์ผ๋กœ ๋ฆฌ)์™€ ์œ„ ์ ๋ง‰ ๋ฐฉ์–ด ์ธ์ž (์œ„ ์ ์•ก, ์ค‘ํƒ„์‚ฐ์—ผ ๋ถ„๋น„, ํ”„๋กœ์Šคํƒ€๊ธ€๋ž€๋”˜ ๋ฐ ์ ๋ง‰ ์„ธํฌ์˜ ์„ ์ฒœ์ ์ธ ์ €ํ•ญ์„ฑ) ์‚ฌ์ด์˜ ๋ถˆ๊ท ํ˜•์œผ๋กœ ๋ฐœ๋ณ‘ํ•œ๋‹ค. ์œ„๊ถค์–‘ ์น˜๋ฃŒ์ œ๋กœ ์—ฌ๋Ÿฌ ์•ฝ๋ฌผ์ด ์‚ฌ์šฉ ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์น˜๋ฃŒ์ œ์—๋Š” ๋ถ€์ž‘์šฉ์ด ์žˆ๋‹ค. ๋ถ€์ž‘์šฉ์ด ์ ์€ ์ƒˆ๋กœ์šด ์œ„์—ผ ์น˜๋ฃŒ์ œ ๊ฐœ๋ฐœ์ด ์ ˆ์‹คํžˆ ์š”๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ์‹๋ฌผ์€ ์ž ์žฌ์ ์ธ ์น˜๋ฃŒ์ œ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์„ ๊ฒƒ์œผ ๋กœ ์ƒ๊ฐ๋˜์–ด ์•ฝ์šฉ ์‹๋ฌผ์—์„œ ์ƒˆ๋กœ์šด ํ™”ํ•ฉ๋ฌผ์„ ์ฐพ๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์ด ๊ณ„์†๋˜๊ณ  ์žˆ๋‹ค. Opuntia ficus-indica (์„ ์ธ์žฅ๊ณผ) (๋ฐฑ๋…„์ดˆ)๋Š” ๋งŽ์€ ๊ตญ๊ฐ€์—์„œ ์ „ํ†ต ์˜์•ฝํ’ˆ์œผ๋กœ ์‚ฌ์šฉ ๋˜๊ณ  ์žˆ๋‹ค. ๋Œ€ํ•œ๋ฏผ๊ตญ์˜ ์ œ์ฃผ๋„์—์„œ๋Š” ๊ฑด๊ฐ• ์‹ํ’ˆ ์ œ์กฐ ์šฉ๋„๋กœ ๋„๋ฆฌ ์žฌ๋ฐฐ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฐฑ๋…„์ดˆ ์—ด๋งค ์ถ”์ถœ๋ฌผ์„ ์œ„์—ผ ์น˜๋ฃŒ ์ฒœ์—ฐ๋ฌผ์˜์•ฝํ’ˆ์œผ๋กœ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜ํ–‰ ๋˜์—ˆ๋‹ค. OF-80E (๋ฐฑ๋…„์ดˆ ์—ด๋งค 80% ์—ํƒ„์˜ฌ ์ถ”์ถœ๋ฌผ)์˜ ํ•ญ๊ถค์–‘ ํ™œ์„ฑ์€ ์—ํƒ„์˜ฌ, ๋น„์Šคํ…Œ๋กœ ์ด๋“œ์„ฑ ํ•ญ์—ผ์ฆ์ œ (์ธ๋„๋ฉ”ํƒ€์‹ , ์•„์Šคํ”ผ๋ฆฐ ๋ฐ ๋””ํด๋กœํŽ˜๋‚™) ๋ฐ ์ŠคํŠธ๋ ˆ์Šค ์œ ๋ฐœ ์œ„์—ผ ๋žซ ํŠธ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ๋Š”, ์‹œํŒ๋œ ์•ฝ๋ฌผ์ธ ์Šคํ‹ฐ๋ Œ์ • (Stillenยฎ tablet) ๋ฐ ๋ฎค์ฝ”์Šคํƒ€์ • (Mucostaยฎ tablet)๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ๋˜ํ•œ, OF-80E์˜ ๊ธ‰์„ฑ๋…์„ฑ, ์•„๋งŒ์„ฑ๋…์„ฑ, ์œ ์ „๋…์„ฑ ๋ฐ ์•ˆ์ „์„ฑ ์•ฝ๋ฆฌ ์—ฐ๊ตฌ๋Š” ์ธ๊ฐ„์˜ ์•ˆ์ „ํ•œ ์„ญ์ทจ๋ฅผ ์œ„ํ•ด ๊ฒฝ์ œํ˜‘๋ ฅ ๊ฐœ๋ฐœ๊ธฐ๊ตฌ ์ง€ ์นจ ๋ฐ ์šฐ์ˆ˜์‹คํ—˜์‹ค๊ด€๋ฆฌ ๊ทœ์ •์— ๋”ฐ๋ผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์œ„์—ผ ๋ชจ๋ธ์—์„œ OF-80E ์˜ ํ•ญ๊ถค์–‘์ž‘์šฉ์˜ ๊ฐ€๋Šฅํ•œ ๋ฉ”์ปค๋‹ˆ์ฆ˜์€ ์ƒํ™”ํ•™ ๋ฐ ๋ถ„์ž ๋ถ„์„์„ ์‚ฌ์šฉํ•˜์—ฌ ์„ค๋ช…ํ•˜์˜€๋‹ค. AGS ์„ธํฌ๋ฅผ ์ด์šฉํ•œ ์•„์Šคํ”ผ๋ฆฐ ์œ ๋„ ์„ธํฌ๋…์„ฑ ์–ต์ œ ์‹œํ—˜์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ฑ๋ถ„ ๋ถ„๋ฆฌ ๋ฅผ ํ†ตํ•˜์—ฌ ๋ฐฑ๋…„์ดˆ ์—ด๋งค๋กœ๋ถ€ํ„ฐ ๋ถ„๋ฆฌํ•˜๊ณ , ์ „์ž์ด์˜จํ™” ์งˆ๋Ÿ‰๋ถ„์„๋ฒ•๊ณผ ํ•ต์ž๊ธฐ ๊ณต๋ช… ๋ถ„ ๊ด‘๋ฒ•์„ ํฌํ•จํ•œ ๋ถ„๊ด‘ ๋ถ„์„์„ ํ†ตํ•ด ๋‘ ์ข…์˜ ํ™œ์„ฑ ํ™”ํ•ฉ๋ฌผ์„ ๋™์ •ํ•˜์˜€๋‹ค. ๋‘ ์ข…์˜ ํ•ญ๊ถค ์–‘ ์„ฑ๋ถ„์€ ํ”Œ๋ผ๋ณด๋…ธ์ด๋“œ์ธ aromadendrin๊ณผ narcissin์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. IC50 ๊ฐ’์œผ๋กœ ๋ณด ๋ฉด, flavone์ธ aromadendrin (<0.5 ฮผM)๊ณผ flavonol์ธ narcissin (<0.5 ฮผM)์€ ๋‹ค๋ฅธ flavones์ธ naringenin (5.9 ฮผM), eriodictyol (>10 ฮผM), taxifolin (1.1 ฮผM) ๋ฐ ๋‹ค๋ฅธ flavonols์ธ kaempferol, quercetin ๋ฐ isokaempfride (>10 ฮผM) ๋ณด๋‹ค AGS ์„ธํฌ๋ฅผ ์ด์šฉํ•œ ์•„์Šคํ”ผ๋ฆฐ ์œ ๋„ ์„ธํฌ๋… ์„ฑ ์–ต์ œ ํšจ๊ณผ๊ฐ€ ์šฐ์ˆ˜ํ•˜์˜€๋‹ค. OF-80E๋Š” ์‹œํŒ๋œ ์•ฝ๋ฌผ๋ณด๋‹ค ๊ณต๊ฒฉ์ธ์ž์— ๋Œ€ํ•ด์„œ ์œ„ ์ ๋ง‰ ์†์ƒ์„ ๋ณดํ˜ธํ•˜๋Š” ํšจ๊ณผ ๊ฐ€ ์šฐ์ˆ˜ํ•˜์˜€๋‹ค. ์—ํƒ„์˜ฌ, ๋น„์Šคํ…Œ๋กœ์ด๋“œ์„ฑ ํ•ญ์—ผ์ฆ์ œ ๋ฐ ์ŠคํŠธ๋ ˆ์Šค ์œ ๋ฐœ ์œ„์—ผ ๋žซํŠธ ๋ชจ๋ธ ์—์„œ, OF-80E๋Š” ์‹œํŒ๋œ ์•ฝ๋ฌผ์— ๋น„ํ•ด ํšจ๊ณผ์ ์œผ๋กœ ์œ„ ์ถœํ˜ˆ์„ฑ ๋ณ‘๋ณ€๊ณผ ์กฐ์งํ•™์  ์กฐ์ง ์†์ƒ์„ ์–ต์ œํ•˜์˜€๋‹ค. ๋‹จํšŒ ๊ฒฝ๊ตฌํˆฌ์—ฌ ๋…์„ฑ์—ฐ๊ตฌ์—์„œ OF-80E์˜ ๊ฐœ๋žต์ ์ธ ์น˜์‚ฌ๋Ÿ‰์€ SD ๋žซํŠธ์˜ ์•”, ์ˆ˜ ๋ชจ๋‘์—์„œ 10000 mg/kg ์ด์ƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. 13์ฃผ ๋ฐ˜๋ณต ๊ฒฝ๊ตฌํˆฌ์—ฌ ๋…์„ฑ์—ฐ๊ตฌ์—์„œ OF- 80E์˜ ๋ฌด๋…์„ฑ๋Ÿ‰์€ SD ๋žซํŠธ์˜ ์•”, ์ˆ˜ ๋ชจ๋‘์—์„œ 2000 mg/kg/day๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. 4์ฃผ ๋ฐ˜ ๋ณต ๊ฒฝ๊ตฌํˆฌ์—ฌ ๋…์„ฑ ์—ฐ๊ตฌ์—์„œ OF-80E์˜ ์ตœ๋Œ€๋‚ด์„ฑ์šฉ๋Ÿ‰์€ ๋น„๊ธ€๊ฒฌ ์•”, ์ˆ˜ ๋ชจ๋‘์—์„œ 1500 mg/kg/day๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. Salmonella typhimurium๊ณผ Escherichia coli๋ฅผ ์ด์šฉํ•œ ๋ณต๊ท€๋Œ์—ฐ ๋ณ€์ด ์‹œํ—˜์—์„œ OF-80E๋Š” ๋Œ์—ฐ๋ณ€์ด๋ฅผ ์œ ๋ฐœํ•˜์ง€ ์•Š์•˜๋‹ค. Chinese hamster lung ์„ธํฌ๋ฅผ ์ด์šฉํ•œ ์—ผ์ƒ‰์ฒด ์ด์ƒ์‹œํ—˜์—์„œ OF-80E๋Š” ์—ผ์ƒ‰์ฒด ์ด์ƒ์„ ์œ ๋ฐœํ•˜์ง€ ์•Š์•˜๋‹ค. ์†Œํ•ต์‹œํ—˜์— ์„œ OF-80E๋Š” ๋™๋ฌผ ๊ณจ์ˆ˜์„ธํฌ์—์„œ ์†Œํ•ต์„ ์œ ๋ฐœํ•˜์ง€ ์•Š์•˜๋‹ค. OF-80E๋ฅผ 5000 mg/kg ์ดํ•˜ ๋กœ ์„ค์น˜๋ฅ˜์— ๋‹จํšŒ ๊ฒฝ๊ตฌ ํˆฌ์—ฌํ–ˆ์„ ๋•Œ, ICR ๋งˆ์šฐ์Šค์˜ ์ค‘์ถ”์‹ ๊ฒฝ๊ณ„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š ์•˜๊ณ , SD ๋žซํŠธ์˜ ํ˜ธํก๊ธฐ๊ณ„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์•˜๋‹ค. OF-80E๋Š” 500 ฮผg/mL ๋†๋„๊นŒ์ง€ human ether-a-go-go related gene ์ฑ„๋„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ๋ณด์•„, OF-80E๋Š” ์‹ฌ ํ˜ˆ๊ด€๊ณ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ๋‚ฎ์„ ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. OF-80E๋Š” AGS ์„ธํฌ์—์„œ ์•„์Šคํ”ผ๋ฆฐ์— ์˜ํ•ด ๊ฐ์†Œ๋˜๊ณ , ๋žซํŠธ์—์„œ ์ธ๋„๋ฉ”ํƒ€์‹ ์— ์˜ํ•ด ๊ฐ์†Œํ•œ glutathione์„ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. OF-80E๋Š” AGS ์„ธํฌ์—์„œ ์•„์Šคํ”ผ๋ฆฐ์— ์˜ํ•ด ๊ฐ ์†Œํ•œ prostaglandin E2์˜ ๋†๋„๋ฅผ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ์ธ๋„๋ฉ”ํƒ€์‹  ์œ ๋„ ์œ„์—ผ ๋žซํŠธ์—์„œ ๊ฐ์†Œ๋œ ๋ถ€์ฐฉ์„ฑ ์œ„์ ์•ก์€ OF-80E์˜ ์ฒ˜๋ฆฌ์— ์˜ํ•ด ํ•ฉ์„ฑ๋˜๊ณ  ๋ถ„๋น„๋˜์—ˆ๋‹ค. OF-80E๋Š” ์ธ๋„๋ฉ”ํƒ€์‹  ์œ ๋„ ๋žซํŠธ์˜ ์œ„ ์ ๋ง‰์—์„œ myeloperoxidase์˜ ํ™œ์„ฑ์„ ์–ต์ œํ•˜๊ณ , ์ŠคํŠธ๋ ˆ์Šค ์œ ๋„ ์œ„์—ผ ๋žซํŠธ์˜ ์œ„ ์ ๋ง‰์—์„œ tumor necrosis factor-ฮฑ๋ฅผ ๊ฐ์†Œ์‹œ์ผฐ๋‹ค.Gastritis is a common disease among Korean adults who take mainly very salty and spicy foods. Usually, symptoms include epigastric pain, nausea, vomiting, abdominal pain, indigestion, and bloating. Pathophysiology of gastritis is due to a lack of equilibrium between the gastric aggressive factor (acid, pepsin, and Helicobacter pylori) and the mucosal defense factor (gastric mucus, bicarbonate secretion, prostaglandins, and innate resistance of the mucosal cells). There are several types of medicines used to treat a gastric ulcer. However, these treatments have side effects. There is a pressing need to develop a new gastritis treatment with fewer side effects. Plants are regarded to represent a reservoir of potential therapeutics and therefore the efforts to search for novel compounds from medicinal plants have been continued. Opuntia ficus-indica (Cactaceae) has been used in traditional medicine of many countries. It is widely cultivated in Jeju Island, Korea, for use in the manufacture of health foods. The aim of this study was to develop O. ficus-indica fruits extract as an antiulcer botanical drug. The antiulcer activity of OF-80E (80% ethanol extract of O. ficus-indica fruits) was assessed using the ethanol-, non-steroidal anti-inflammatory drugs (indomethacin, aspirin, and diclofenac)-, and stress-induced gastritis rat models. The results were compared with those of commercially available drugs, Stillenยฎ tablet and Mucostaยฎ tablet. In addition, the acute toxicity, sub-chronic toxicity, genotoxicity, and safety pharmacology studies of OF-80E were analyzed under Organization for Economic Cooperation and Development guideline and Good Laboratory Practice regulations for human safe consumption. Finally, the possible mechanism underlying the antiulcer actions of OF-80E in gastritis models were elucidated using biochemical and molecular analyses. Inhibition of aspirin-induced cytotoxicity in AGS cells assay-guided fractionation of the O. ficus-indica fruits led to the identification of two active compounds through spectroscopic analyses, including electron ionization mass spectrometry and nuclear magnetic resonance spectroscopy. The two antiulcer constituents were the flavonoids aromadendrin and narcissin. Based on the IC50 values, the flavone, aromadendrin (10 ฮผM) and taxifolin (1.1 ฮผM), and flavonols, kaempferol, quercetin, and isokaempfride (>10 ฮผM). OF-80E was more effective than commercially available drugs to protect gastric mucosal damage against aggressive factors. In ethanol-, non-steroidal anti-inflammatory drugs-, and stress-induced gastritis rat models, OF-80E inhibited gastric hemorrhagic lesions and histological tissue damage effectively comparing than commercially available drugs. In a single dose oral toxicity study, the approximate lethal dose of OF-80E in both male and female of Sprague Dawley (SD) rats was higher than 10000 mg/kg. In a 13-week repeated oral toxicity study, the no observed adverse effect level of OF-80E was 2000 mg/kg/day for both sexes of SD rats. In a 4-week repeated oral toxicity study, the maximum tolerance dose of OF-80E was 1500 mg/kg/day for both sexes of beagle dogs. In Salmonella typhimurium and Escherichia coli reverse mutation studies, OF-80E did not cause mutation. In a chromosome aberration test, OF-80E did not cause chromosomal aberration in Chinese hamster lung cells. In micronucleus assay, OF-80E did not induce micronuclei in the mammalian bone marrow cells. Single oral administration of OF-80E to rodent at below 5000 mg/kg did not affect the central nervous system of ICR mice and did not induce adverse effects on the respiratory system of SD rats. OF-80E did not effect on the human ether-a-go-go related gene channel up to the concentration of 500 ฮผg/mL, indicating that the effect of OF-80E on cardiovascular system was to be low. OF-80E increased glutathione reduced by aspirin in AGS cells and decreased by indomethacin in rats. OF-80E increased prostaglandin E2 levels reduced by aspirin in AGS cells. Decreased adherent mucus was synthesized and stimulated, by OF-80E pretreatment in indomethacin-induced gastritis rats. OF-80E inhibited myeloperoxidase activity in indomethacin-induced rat gastric mucosal and reduced tumor necrosis factor-ฮฑ in stress-induced rat gastric mucosal.Abstract i Contents iv List of Abbreviations xi List of Figures xiv List of Tables xvi โ… . Introduction ๏ผ‘ โ…ก. Literature reviews ๏ผ” 1. Gastritis (Gastric ulcer) ๏ผ” 1.1. Pathophysiology and risk factors ๏ผ• 1.1.1. Helicobacter pylori ๏ผ– 1.1.2. Nonsteroidal anti-inflammatory drugs ๏ผ˜ 1.2. Morbidity and mortality ๏ผ™ 1.3. Diagnosis ๏ผ‘๏ผ 1.4. Prevention ๏ผ‘๏ผ’ 2. Therapeutic agents of gastritis ๏ผ‘๏ผ’ 2.1. Histamine H2 antagonists ๏ผ‘๏ผ• 2.2. Proton pump inhibitors ๏ผ‘๏ผ• 2.3. Antacids ๏ผ‘๏ผ— 2.4. Prostaglandin derivatives ๏ผ‘๏ผ˜ 2.5. Antimuscarinic agents ๏ผ‘๏ผ˜ 2.6. Anti-Helicobacter pylori therapy ๏ผ‘๏ผ™ 2.6.1. Combination therapy ๏ผ’๏ผ 2.6.2. Anti-Helicobacter pylori vaccines ๏ผ’๏ผ‘ 2.7. CCKB antagonists ๏ผ’๏ผ’ 3. Study of new drug from plant extracts to treat gastritis ๏ผ’๏ผ“ 3.1. Plant extracts with antigastritis activity ๏ผ’๏ผ“ 3.2. Phytochemicals with antigastritis activity ๏ผ’๏ผ— 3.3. Herbal medicines tested in clinical trials ๏ผ“๏ผ 4. Opuntia ficus-indica (L.) Miller ๏ผ“๏ผ” 4.1. Nutritional contents and bioactive constituents of Opuntia ficus-indica ๏ผ“๏ผ• 4.2. Biological activities of Opuntia ficus-indica ๏ผ“๏ผ™ 4.2.1. Flower ๏ผ”๏ผ 4.2.2. Fruit/pulp ๏ผ”๏ผ‘ 4.2.3. Seed ๏ผ”๏ผ’ 4.2.4. Peel/skin ๏ผ”๏ผ“ 4.2.5. Cladode ๏ผ”๏ผ” โ…ข. Materials and Methods ๏ผ”๏ผ• 1. Preparation of test materials ๏ผ”๏ผ• 1.1. Instrumental analysis ๏ผ”๏ผ• 1.2. Chemicals and reagents ๏ผ”๏ผ– 1.3. Preparation of O. ficus-indica fruit ethanol extracts ๏ผ”๏ผ– 1.4. Bioassay-guided fractionation and isolation of O. ficus-indica fruits ๏ผ”๏ผ— 1.5. High-performance liquid chromatography with diode array detector and electrospray ionization mass spectrometry chemical analysis ๏ผ”๏ผ™ 1.6. High-performance liquid chromatography analysis of narcissin and aromadendrin ๏ผ•๏ผ 1.7. High-performance liquid chromatography analysis of betanin ๏ผ•๏ผ 1.8. Mass production of OF-80E (O. ficus-indica fruits 80% ethanol extract) ๏ผ•๏ผ‘ 2. Evaluation of gastro-protective activity in an in vitro model ๏ผ•๏ผ‘ 2.1. Gastric AGS cell cultures ๏ผ•๏ผ‘ 2.2. Ethanol-induced cytotoxicity in AGS cells ๏ผ•๏ผ’ 2.3. Aspirin-induced cytotoxicity in AGS cells ๏ผ•๏ผ’ 2.4. Determination of reduced glutathione level in AGS cells ๏ผ•๏ผ“ 2.5. Determination prostaglandin E2 level in AGS cells ๏ผ•๏ผ“ 2.6. Data analysis ๏ผ•๏ผ” 3. Evaluation of gastro-protective activity in an in vivo model ๏ผ•๏ผ” 3.1. Animals ๏ผ•๏ผ” 3.2. Ethanol-induced gastritis rats ๏ผ•๏ผ• 3.3. Indomethacin-induced gastritis rats ๏ผ•๏ผ• 3.4. Aspirin-induced gastritis rats ๏ผ•๏ผ• 3.5. Stress-induced gastritis rats ๏ผ•๏ผ– 3.6. Diclofenac-induced gastritis rats ๏ผ•๏ผ– 3.7. Determination of gastric lesion index ๏ผ•๏ผ— 3.8. Gastric adherent mucus assay ๏ผ•๏ผ— 3.9. Measurement of mucosal myeloperoxidase and tumor necrosis factor-alpha ๏ผ•๏ผ˜ 3.10. Measurement of reduced glutathione level of mucosa ๏ผ•๏ผ˜ 3.11. Measurement of histological index of gastric tissue ๏ผ•๏ผ˜ 3.12. Data analysis ๏ผ•๏ผ™ 4. Toxicity studies of OF-80E ๏ผ•๏ผ™ 4.1. Single dose oral toxicity study in Sprague Dawley rats ๏ผ•๏ผ™ 4.1.1. Animals ๏ผ•๏ผ™ 4.1.2. Experimental design ๏ผ–๏ผ 4.1.3. Data analysis ๏ผ–๏ผ‘ 4.2. Thirteen-week repeated-dose oral toxicity study with a four-week recovery in Sprague Dawley rats ๏ผ–๏ผ‘ 4.2.1. Experimental design ๏ผ–๏ผ‘ 4.2.2. Urine collection and blood sampling ๏ผ–๏ผ‘ 4.2.3. Urinalysis ๏ผ–๏ผ’ 4.2.4. Hematological test ๏ผ–๏ผ’ 4.2.5. Clinical biochemistry test ๏ผ–๏ผ“ 4.2.6. Histopathology ๏ผ–๏ผ“ 4.2.7. Data analysis ๏ผ–๏ผ” 4.3. Four-week repeated-dose oral toxicity study in beagle dogs ๏ผ–๏ผ• 4.3.1. Animals ๏ผ–๏ผ• 4.3.2. Experimental design ๏ผ–๏ผ– 4.3.3. Urine collection and blood sampling ๏ผ–๏ผ– 4.3.4. Urinalysis ๏ผ–๏ผ— 4.3.5. Hematological test ๏ผ–๏ผ— 4.3.6. Clinical biochemistry test ๏ผ–๏ผ— 4.3.7. Histopathology ๏ผ–๏ผ˜ 4.4. Bacterial reverse mutation test ๏ผ–๏ผ˜ 4.4.1. Test strains and materials preparation ๏ผ–๏ผ˜ 4.4.2. Experimental procedures ๏ผ–๏ผ™ 4.5. Chromosome aberration test in CHL cells ๏ผ—๏ผ 4.5.1. Test system ๏ผ—๏ผ 4.5.2. Experimental procedure ๏ผ—๏ผ‘ 4.5.3. Evaluation of chromosomal aberration ๏ผ—๏ผ’ 4.6. In vivo micronucleus assay ๏ผ—๏ผ“ 4.6.1. Test system ๏ผ—๏ผ“ 4.6.2. Observations and examinations ๏ผ—๏ผ” 4.7. Effect of OF-80E on the central nervous system in ICR mice ๏ผ—๏ผ• 4.7.1. Test system ๏ผ—๏ผ• 4.7.2. Observations and examinations ๏ผ—๏ผ– 4.8. Effect of OF-80E on the respiratory rate and tidal volume in Sprague Dawley rats ๏ผ—๏ผ™ 4.8.1. Test system ๏ผ—๏ผ™ 4.8.2. Observations and examinations ๏ผ—๏ผ™ 4.9. Effect of OF-80E on human ether-a-go-go-related gene potassium channel expressed in Chinese hamster ovary cells ๏ผ˜๏ผ 4.9.1. Cells cultures ๏ผ˜๏ผ 4.9.2. Preparation of test substances and test solution ๏ผ˜๏ผ‘ 4.9.3. Measurement and analysis ๏ผ˜๏ผ‘ 4.9.4. Data analysis ๏ผ˜๏ผ’ โ…ฃ. Results ๏ผ˜๏ผ” 1. Gastro-protective activity of O. ficus-indica fruits in an in vitro model ๏ผ˜๏ผ” 1.1. Activity comparisons of various O. ficus-indica fruit ethanol extracts on aspirin-induced cytotoxicity in AGS cells ๏ผ˜๏ผ” 1.2. Chemical constituent of O. ficus-indica fruit ethanol extracts ๏ผ˜๏ผ• 1.3. Bioassay-guided fractionation and identification of O. ficus-indica fruits ๏ผ˜๏ผ• 1.4. Effect of the isolated flavonoids on aspirin-induced cytotoxicity in AGS cells ๏ผ™๏ผ‘ 1.5. Effect of OF-80E on ethanol-induced cytotoxicity in AGS cells ๏ผ™๏ผ’ 1.7. Effect of OF-80E on aspirin-induced cytotoxicity in AGS cells ๏ผ™๏ผ“ 1.8. Reduced glutathione and prostaglandin E2 levels in AGS cells treated with OF-80E ๏ผ™๏ผ” 2. Gastro-protective activity of O. ficus-indica fruits in an in vivo model ๏ผ™๏ผ– 2.1. Gastro-protective activity of OF-80E in an ethanol-induced gastritis rat ๏ผ™๏ผ– 2.2. Gastro-protective activity of OF-80E in an indomethacin-induced gastritis rat ๏ผ‘๏ผ๏ผ 2.3. Gastro-protective activity of OF-80E in an aspirin-induced gastritis rat ๏ผ‘๏ผ๏ผ’ 2.4. Gastro-protective activity of OF-80E in a stress-induced gastritis rat ๏ผ‘๏ผ๏ผ” 2.5. Gastro-protective activity of OF-80E in a diclofenac-induced gastritis rat ๏ผ‘๏ผ๏ผ– 2.6. Anti-inflammatory and antioxidative activity of OF-80E ๏ผ‘๏ผ๏ผ˜ 2.6.1. Effect of OF-80E on membrane-bound myeloperoxidase activity ๏ผ‘๏ผ๏ผ˜ 2.6.2. Effect of OF-80E on tumor necrosis factor-ฮฑ level ๏ผ‘๏ผ๏ผ™ 2.6.3. Effect of OF-80E on reduced glutathione level ๏ผ‘๏ผ๏ผ™ 2.7. Effect of OF-80E on adherent mucus level ๏ผ‘๏ผ‘๏ผ 3. Toxicity studies of OF-80E ๏ผ‘๏ผ‘๏ผ‘ 3.1. Single dose oral toxicity study in Sprague Dawley rats ๏ผ‘๏ผ‘๏ผ‘ 3.2. Thirteen-week repeated-dose oral toxicity study in Sprague Dawley rats ๏ผ‘๏ผ‘๏ผ” 3.2.1. General clinical signs ๏ผ‘๏ผ‘๏ผ” 3.2.2. Body weights ๏ผ‘๏ผ‘๏ผ– 3.2.3. Food consumption ๏ผ‘๏ผ‘๏ผ– 3.2.4. Water consumption ๏ผ‘๏ผ’๏ผ 3.2.5. Urinalysis ๏ผ‘๏ผ’๏ผ“ 3.2.6. Hematological test ๏ผ‘๏ผ’๏ผ™ 3.2.7. Clinical blood biochemistry test ๏ผ‘๏ผ“๏ผ 3.2.8. Organ weights ๏ผ‘๏ผ“๏ผ 3.2.9. Necropsy finding ๏ผ‘๏ผ“๏ผ‘ 3.2.10. Histopathological examination ๏ผ‘๏ผ“๏ผ’ 3.3. Four-week repeated-dose oral toxicity study in beagle dogs ๏ผ‘๏ผ“๏ผ“ 3.4. Bacterial reverse mutation study ๏ผ‘๏ผ“๏ผ– 3.5. Chromosome aberration test in CHL cells ๏ผ‘๏ผ“๏ผ˜ 3.5.1. Results in the presence of S9 mixture ๏ผ‘๏ผ“๏ผ˜ 3.5.2. Results in the absence of S9 mixture ๏ผ‘๏ผ“๏ผ˜ 3.6. Frequency of micronucleated polychromatic erythrocyte and cytotoxicity in an in vivo micronucleus assay ๏ผ‘๏ผ”๏ผ‘ 3.7. Effect of OF-80E on the central nervous system in ICR mice ๏ผ‘๏ผ”๏ผ“ 3.8. Effect of OF-80E on the respiratory rate and tidal volume in Sprague Dawley rats ๏ผ‘๏ผ”๏ผ– 3.9. Effect of OF-80E on human ether-a-go-go-related gene potassium channel expressed in Chinese hamster ovary cells ๏ผ‘๏ผ”๏ผ˜ โ…ค. Discussion ๏ผ‘๏ผ•๏ผ References ๏ผ‘๏ผ•๏ผ˜ Abstract in Korean ๏ผ‘๏ผ˜๏ผ™Docto

    ์‹ฌ์žฅ ๋ถ„ํ• ์„ ์œ„ํ•œ ์œค๊ณฝ์„  ๋ฐ ๊ฑฐ๋ฆฌ ๋ณ€ํ™˜ ๊ธฐ๋ฐ˜ ๋ชจ์–‘ ์ธ์‹ ์–ดํ…์…˜ ๋„คํŠธ์›Œํฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ์‹ ์˜๊ธธ.Cardiac image segmentation is an important task in the development of clinical cardiac applications. Many recent studies came up with deep learning models, mostly composed of convolutional neural networks(CNN), and showed significant outcomes on the segmentation of target organs in medical images. Unlike other major biological structures such as lungs and liver, the cardiac organ consists of multiple substructures. Those cardiac substructures are intimately adjacent to each other, which means that the segmentation network should concentrate on the boundaries of the substructures. In this paper, to increase the performance of cardiac image segmentation, we introduce a novel model to learn shape-aware and boundary-aware features using the distance transformation and the contour image of the labeled data. We present a shape-aware attention module that can guide a model to focus on edges between structures. we also propose the regularization for refining the contour probabilistic map. The experimental results show that the proposed network produces more accurate results compared to state-of-the-art networks by obtaining 4.97\% more in terms of dice similarity coefficient(DSC) score. We used 20 CT cardiac images for training and validation, and 40 CT cardiac images for the test. Moreover, our segmentation results describe that learning precise contour and distance transform features can help improve model performance. The ablations studies are presented to emphasize the importance of our proposed shape-aware attention mechanism.์‹ฌ์žฅ ์˜์ƒ ๋ถ„ํ• ์€ ์‹ฌ์žฅ ์˜๋ฃŒ ์ˆ˜์ˆ ์˜ ์„ค๊ณ„์— ์žˆ์–ด ์ค‘์š”ํ•œ ๊ณผ์ •์ด๋‹ค. ์ตœ๊ทผ์—๋Š” ์˜๋ฃŒ ์˜์ƒ์—์„œ ์žฅ๊ธฐ ๋ถ„ํ• ์„ ์œ„ํ•ด ๋”ฅ๋Ÿฌ๋‹์„ ์ด์šฉํ•œ CNN๋“ค์ด ๋งŽ์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ๋‹ค๋ฅธ ์ฃผ์š” ์ธ์ฒด์˜ ์žฅ๊ธฐ๋“ค์— ๋น„ํ•ด, ์‹ฌ์žฅ์€ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ๋ถ€๋ถ„ ์žฅ๊ธฐ๋“ค๋กœ ๊ตฌ์„ฑ๋˜์–ด์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์‹ฌ์žฅ์˜ ๋ถ€๋ถ„ ์žฅ๊ธฐ๋“ค์€ ๋งค์šฐ ์ธ์ ‘ํ•˜์—ฌ ์กด์žฌํ•˜๊ธฐ ๋–„๋ฌธ์— CNN์€ ์žฅ๊ธฐ๋“ค์˜ ๊ฒฝ๊ณ„์„  ๋ถ€๋ถ„์— ์ง‘์ค‘ํ•˜์—ฌ ํ•™์Šต๋˜์–ด์•ผํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹ฌ์žฅ ๋ถ„ํ•  ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ์„ ๋†’์ด๊ธฐ ์œ„ํ•˜์—ฌ distance transform๊ณผ ์œค๊ณฝ์„  ์˜์ƒ์„ ํ™œ์šฉํ•˜์—ฌ ๋ชจ์–‘ ์ธ์‹ ํŠน์ง•๋งต ๊ทธ๋ฆฌ๊ณ  ๊ฒฝ๊ณ„์„  ์ธ์‹ ํŠน์ง•๋งต์„ ํ•™์Šตํ•  ์ˆ˜ ์žˆ๋Š” ๋„คํŠธ์›Œํฌ ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ถ€๋ถ„ ์žฅ๊ธฐ๋“ค์˜ ๊ฒฝ๊ณ„์„  ํŠน์ง•์„ ๋ชจ๋ธ ํ•™์Šต์— ๋„์™€์ค„ ์ˆ˜ ์žˆ๋Š” ๋ชจ์–‘ ์ธ์‹ ๊ทธ๋ฆฌ๊ณ  ๊ฒฝ๊ณ„์„  ์ธ์‹ ํŠน์ง• attention ๊ธฐ์ˆ ์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ ์ •ํ™•ํ•œ ๊ฒฝ๊ณ„์„  ํ™•๋ฅ ๋งต์„ ์ฐพ๊ธฐ ์œ„ํ•œ ์ •๊ทœํ™” ๋ฐฉ๋ฒ•๋„ ์ œ์•ˆํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋“ค์€ ์ œ์•ˆํ•œ ๋„คํŠธ์›Œํฌ๊ฐ€ ๋‹ค๋ฅธ ์ตœ์‹ ์˜ ์˜์ƒ ๋ถ„ํ•  ๋„คํŠธ์›Œํฌ๋“ค์— ๋น„ํ•ด 4.97\% ๋†’์€ DSC ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์คŒ์œผ๋กœ ์˜์ƒ ๋ถ„ํ•  ๊ฒฐ๊ณผ๊ฐ€ ๋”์šฑ ์ •ํ™•ํ•จ์„ ์•Œ๋ ค์ค€๋‹ค. ์˜์ƒ ๋ถ„ํ•  ๋„คํŠธ์›Œํฌ์˜ ํ•™์Šต๊ณผ ๊ฒ€์ฆ์— ์žˆ์–ด 20๊ฐœ์˜ ์‹ฌ์žฅ CT๋ฅผ ์‚ฌ์šฉํ•˜์˜€๊ณ , 40๊ฐœ์˜ ์˜์ƒ์„ ์‹คํ—˜์— ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ด์— ๋”ํ•ด, ์ œ์•ˆํ•œ ๋„คํŠธ์›Œํฌ์˜ ๋ถ„ํ•  ๊ฒฐ๊ณผ๋Š” ์ •ํ™•ํ•œ ๊ฒฝ๊ณ„๋ฉด๊ณผ distance transform์„ ์–ป๋Š” ๊ฒƒ์ด ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ์„ ๋†’์—ฌ์ค„ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ œ์•ˆํ•œ ๋ชจ์–‘ ์ธ์‹ attention ๋ฐฉ๋ฒ•์˜ ๊ฒ€์ฆ๊ณผ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•˜๊ธฐ ์œ„ํ•ด ablation study๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค.Chapter 1 Introduction 1 Chapter 2 Backgrounds 5 2.1 Deep Neural Networks for Image Segmentation 5 2.1.1 Overview 5 2.1.2 Convolutional Layer 6 2.1.3 Residual Connection 8 2.2 Visual Attention Mechanism 9 Chapter 3 Related Works 11 3.1 CNNs for Biomedical Image Segmentation 11 3.2 Distance Transformation for CNN Image Segmentation 14 3.2.1 Distance Transformation(DT) 14 3.2.2 DT-based Image Segmentation 15 Chapter 4 Methodology 18 4.1 Overview 18 4.2 CDA-Net Architecture 18 4.2.1 DT Regression for Image Segmentation19 4.2.2 Shape-aware Attention 21 4.2.3 Regularization with the Penalty Term to Refine Feature 22 4.3 Overall Loss Function 23 4.4 Data Preparation 25 Chapter 5 Experimental Results 28 5.1 Dataset 28 5.2 Evaluation Metrics 29 5.3 Experiments Detail 30 5.4 Comparison with State-of-the-Art 30 5.5 Ablation Study 31 Chapter 6 Conclusion 37 Bibliography 39 ์ดˆ๋ก 45Maste

    Piceatannol reduces resistance to statins in hypercholesterolemia by reducing PCSK9 expression through p300 acetyltransferase inhibition

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    The purpose of this study was to investigate the role of piceatannol (PT) in statin (rosuvastatin and simvastatin) resistance and tolerance and its association with PCSK9 expression via its p300 inhibitory (p300i) activity. An in vitro study was performed using HepG2 cells that were exposed to statins (rosuvastatin or simvastatin) with or without PT in delipidated serum (DLPS) medium. In the statin exposed conditions, PCSK9 expression was reduced following PT treatment when compared to HepG2 cells w/o PT treatment. Furthermore, no significant difference was observed in the expression of the transcription factors SREBP2 and HNF1ฮฑ, which regulate PCSK9 expression. This resulted in low density lipoprotein receptor (LDLR) stabilization and reduced cellular cholesterol levels. This indicates that PT epigenetically controls statin-induced PCSK9 expression. Interestingly, PT attenuated p300 histone acetyltransferase (HAT) activity. Moreover, simulation of PT-p300 binding suggested that PT inhibits p300 as PT could be docked in the p300 HAT domain. Furthermore, inhibition of p300 HAT activity using C-646, a selective p300 inhibitor, or through an siRNA system effectively reduced PCSK9 induction upon statin exposure in HepG2 cells. The chromatin immunoprecipitation (ChIP) assays revealed that PT blocked the recruitment of p300 to the PCSK9 promoter region. In summary, PT attenuated statin-induced PCSK9 expression by inhibiting p300 HAT activity. Finally, co-administration of simvastatin and PT for 10 weeks further reduced plasma low-density lipoprotein-cholesterol (LDL-C) levels and stabilized the hepatic LDLR protein level compared with those resulting from single treatment of simvastatin in a high-fat diet-induced hypercholesterolemia mouse model. Our findings indicate that PT is a new nutraceutical candidate to reduce the statin resistance and tolerance that occurs in patients with hypercholesterolemia.ope

    Post-transcriptional regulation of low density lipoprotein receptor protein by proprotein convertase subtilisin/kexin type 9a in mouse liver

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    Lipid homeostasis is transcriptionally regulated by three DNA-binding proteins, designated sterol regulatory element-binding protein (SREBP)-1a, -1c, and -2. Oligonucleotide arrays hybridized with RNA made from livers of transgenic SREBP-1a, transgenic SREBP-2, and SREBP cleavage-activating protein knockout mice recently identified 33 genes regulated by SREBPs in liver, four of which had no known connection to lipid metabolism. One of the four genes was PCSK9, which encodes proprotein convertase subtilisin/kexin type 9a, a protein that belongs to the proteinase K subfamily of subtilases. Mutations in PCSK9 are associated with an autosomal dominant form of hypercholesterolemia. Here, we demonstrate that hepatic overexpression of either wild-type or mutant PCSK9 in mice results in hypercholesterolemia. The hypercholesterolemia is due to a post-transcriptional event causing a reduction in low density lipoprotein (LDL) receptor protein prior to the internalization and recycling of the receptor. Overexpression of PCSK9 in primary hepatocytes and in mice lacking the LDL receptor does not alter apolipoprotein B secretion. These data are consistent with PCSK9 affecting plasma LDL cholesterol levels by altering LDL receptor protein levels via a post-transcriptional mechanism.ope

    Implementation of multi-dimensional flow analysis capability in MARS-KS motion model for marine reactor safety analysis

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์—๋„ˆ์ง€์‹œ์Šคํ…œ๊ณตํ•™๋ถ€, 2021. 2. ์กฐํ˜•๊ทœ.In 2018, International Maritime Organization(IMO) adopted an initial strategy on the reduction of greenhouse gas emissions from ships to reduce the total annual greenhouse gas emissions by at least 50% by 2050 compared to 2008 levels. Thus, researches for marine reactor development as eco-friendly energy and safety analysis of marine reactor are actively being conducted. It is expected that new regulatory demand arises, and fundamental research is required to secure independent verification capability at the level of regulatory agencies. In this background, an improvement study for the MARS-KS motion model was conducted for marine reactor safety analysis. The goal of the study is to implement the multi-dimensional flow analysis capability in MARS-KS motion model. The research was conducted in the order of code improvement, verification, and application study. First, the 7 code improvements were performed. Improvement of volume direction unit vector generation method for user-friendly code implementation, correction of linear acceleration term considering inertial force, correction of the formula for the volumes position under the rotational condition, update of junction property, calculation of pressure drop along each axis and improvement of connection information formula between connected volumes to realize multi-dimensional flow analysis. And improvement study was conducted to extend the MARS-KS motion model to the MULTID component. The second is the verification of the improved code. First, 1D component and cross-flow verification was performed by solving the conceptual problem used in the previous research for the MARS-KS motion model at Seoul National University(H.K. Beom et al., 2019). For the verification of the 1D component, the manometer and vertical pipe conceptual problem were analyzed under various motion conditions, and analysis results were compared quantitatively with the analytical solution. Next, for cross-flow verification, a conceptual problem simulating the downcomer in the reactor was selected by connecting 1D vertical pipes with a cross-junction. When simulating the downcomer in the reactor, the six-pipe and eight-pipe conceptual problems that could be implemented through this study were verified with the existing conceptual problem consisting of four-pipe. Following the verification of 1D component and cross-flow, the conceptual problems for a 3D slab and an annular 3D cylinder were selected for the verification of the MULTID component. Likewise, it was analyzed under various motion conditions. In addition, analysis capability of the motion model for MULTID component was confirmed by applying the motion conditions in which various external forces act on the 3D slab. Finally, the modified code was used to predict the thermal-hydraulic phenomena, and the change of flow instability and critical heat flux under motion conditions was examined. First, the results of predicting the flow instability under two-phase conditions through the RELAP5 code(M. Colombo et al., 2012) were compared with the MARS-KS analysis results. Next, utilizing the MARS-KS motion model, the unstable region that appears due to flow instability in stationary and motion conditions was predicted and compared. The change of critical heat flux due to dynamic motion under the same heat flux condition was also examined using the same analytical model. When the critical heat flux appears, the heat transfer efficiency to the working fluid greatly decreases, thus the wall temperature increases rapidly. From this, it was confirmed whether the critical heat flux occurs in the stationary and motion conditions. Through this application study, the usability of the improved code was verified.2018๋…„ ๊ตญ์ œํ•ด์‚ฌ๊ธฐ๊ตฌ(IMO)๋Š” IMO ์„ ๋ฐ• ์˜จ์‹ค๊ฐ€์Šค ๊ฐ์ถ• ์ดˆ๊ธฐ์ „๋žต์„ ์ฑ„ํƒํ•˜์—ฌ ์„ ๋ฐ•์—์„œ ๋ฐฐ์ถœ๋˜๋Š” ์˜จ์‹ค๊ฐ€์Šค๋ฅผ 2050๋…„๊นŒ์ง€ 2008๋…„ ๋Œ€๋น„ 50% ๊ฐ์ถ•ํ•˜๊ธฐ ์œ„ํ•œ ๊ณ„ํš์„ ์ˆ˜๋ฆฝํ•˜์˜€๋‹ค. ๊ตญ์ œํ•ด์‚ฌ๊ธฐ๊ตฌ์˜ ์กฐ์น˜์— ๋”ฐ๋ผ ์นœํ™˜๊ฒฝ ์—๋„ˆ์ง€๋กœ์จ ํ•ด์–‘์›์ „์„ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•œ ๊ตญ๋‚ดยท์™ธ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ์‚ฐํ•™์—ฐ์—์„œ ํ•ด์–‘์›์ „ ๊ฐœ๋ฐœ๊ณผ ์•ˆ์ „์„ฑ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋จ์— ๋”ฐ๋ผ ์‹ ๊ทœ ๊ทœ์ œ์ˆ˜์š”์˜ ๋ฐœ์ƒ์ด ์˜ˆ์ƒ๋˜๋ฉฐ, ๊ทœ์ œ๊ธฐ๊ด€ ์ฐจ์›์˜ ๋…์ž์ ์ธ ๊ฒ€์ฆ๋Šฅ๋ ฅ ํ™•๋ณด๋ฅผ ์œ„ํ•œ ๊ธฐ๋ฐ˜ ์—ฐ๊ตฌ๊ฐ€ ์š”๊ตฌ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฐ๊ฒฝ์—์„œ ํ•ด์–‘์›์ „์˜ ์•ˆ์ „์„ฑ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ MARS-KS motion model์˜ ๊ฐœ์„ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ชฉํ‘œ๋Š” MARS-KS motion model์˜ ๋‹ค์ฐจ์› ์œ ๋™ ํ•ด์„๋Šฅ๋ ฅ์˜ ๊ตฌํ˜„์ด๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ์ฝ”๋“œ ๊ฐœ์„ , ๊ฐœ๋…๋ฌธ์ œ๋ฅผ ํ†ตํ•œ ๊ฒ€์ฆ, ๊ทธ๋ฆฌ๊ณ  ๊ฐœ์„ ๋œ ์ฝ”๋“œ๋ฅผ ํ™œ์šฉํ•œ ์‘์šฉ ์ˆœ์œผ๋กœ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ MARS-KS motion model์— ๋Œ€ํ•ด ๋‹ค์Œ๊ณผ ๊ฐ™์€ 7๊ฐ€์ง€ ์‚ฌํ•ญ์„ ๊ฐœ์„ ํ•˜์˜€๋‹ค. ์‚ฌ์šฉ์ž ์นœํ™”์ ์ธ ์ฝ”๋“œ ๊ตฌํ˜„์„ ์œ„ํ•œ Volume direction unit vector ์ƒ์„ฑ๋ฐฉ๋ฒ•์˜ ๊ฐœ์„ , ๊ด€์„ฑ๋ ฅ์„ ๊ณ ๋ คํ•œ ์ง์„ ๊ฐ€์†๋„ ํ•ญ ์ˆ˜์ •, ํšŒ์ „ ์ค‘์‹ฌ์— ๊ด€ํ•œ ์˜ค๋ฅ˜ ์ˆ˜์ •, ๋งค์‹œ๊ฐ„ ๋‹จ๊ณ„๋งˆ๋‹ค ์ •์…˜(Junction) ์ •๋ณด ์—…๋ฐ์ดํŠธ, ๋‹ค์ฐจ์› ์œ ๋™์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•œ ์ถ•๋ณ„ ์••๋ ฅ ์ˆ˜๋‘ ๊ณ„์‚ฐ ๋ฐ ์—ฐ๊ฒฐ์ •๋ณด ๊ณ„์‚ฐ์‹์˜ ๊ฐœ์„ , ๊ทธ๋ฆฌ๊ณ  MARS-KS motion model์„ MULTID ์ปดํฌ๋„ŒํŠธ๋กœ ํ™•์žฅํ•˜๊ธฐ ์œ„ํ•œ ๊ฐœ์„ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋Š” ๊ฐœ์„ ๋œ MARS-KS motion model์— ๋Œ€ํ•œ ๊ฒ€์ฆ์ด๋‹ค. ๋จผ์ €, ์„œ์šธ๋Œ€ํ•™๊ต์—์„œ MARS-KS motion model์˜ ๋ฌผ๋ฆฌ์  ๋ชจ๋ธ ๊ฒ€์ฆ(H.K. Beom et al., 2019) ์‹œ ์‚ฌ์šฉํ•˜์˜€๋˜ ๊ฐœ๋…๋ฌธ์ œ ํ’€์ด๋ฅผ ํ†ตํ•ด 1D ์ปดํฌ๋„ŒํŠธ์™€ ๊ต์ฐจ์œ ๋™(Cross-flow)์— ๋Œ€ํ•œ ๊ฒ€์ฆ์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. 1D ์ปดํฌ๋„ŒํŠธ์˜ ๊ฒ€์ฆ์„ ์œ„ํ•ด 1์ฐจ์› ๋งˆ๋…ธ๋ฏธํ„ฐ์™€ ์ˆ˜์ง ํŒŒ์ดํ”„ ๊ฐœ๋…๋ฌธ์ œ๋ฅผ ๋‹ค์–‘ํ•œ ์šด๋™ ์กฐ๊ฑด์—์„œ ํ•ด์„ํ•˜์˜€๊ณ , ์ด๋ก ํ•ด์™€ ์ •๋Ÿ‰์ ์œผ๋กœ ๋น„๊ตํ•˜์—ฌ ์ฝ”๋“œ์˜ ์ ์ ˆ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ๊ต์ฐจ์œ ๋™ ๊ฒ€์ฆ์„ ์œ„ํ•ด ๋‹จ์ƒ ์กฐ๊ฑด์˜ 1์ฐจ์› ์ˆ˜์ง ํŒŒ์ดํ”„๋ฅผ ๊ต์ฐจ์ •์…˜(Cross-junction)์œผ๋กœ ์—ฐ๊ฒฐํ•˜์—ฌ ๊ฐ•์ˆ˜๋ถ€๋ฅผ ๋ชจ์‚ฌํ•œ ๊ฐœ๋…๋ฌธ์ œ๋ฅผ ์„ ์ •ํ•˜์˜€๋‹ค. ๊ฐ•์ˆ˜๋ถ€๋ฅผ ๋ชจ์‚ฌํ•  ๋•Œ, 4๊ฐœ ํŒŒ์ดํ”„๋กœ ๊ตฌ์„ฑ๋œ ๊ธฐ์กด ๊ฐœ๋…๋ฌธ์ œ์™€ ํ•จ๊ป˜ ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋œ 6๊ฐœ ํŒŒ์ดํ”„์™€ 8๊ฐœ ํŒŒ์ดํ”„ ๊ฐœ๋…๋ฌธ์ œ์— ๋Œ€ํ•œ ๊ฒ€์ฆ์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. 1D ์ปดํฌ๋„ŒํŠธ์™€ ๊ต์ฐจ์œ ๋™ ๊ฒ€์ฆ์— ์ด์–ด MULTID ์ปดํฌ๋„ŒํŠธ ๊ฒ€์ฆ์„ ์œ„ํ•ด 3์ฐจ์› ์Šฌ๋ž˜๋ธŒ์™€ ํ™˜ํ˜•์˜ 3์ฐจ์› ์‹ค๋ฆฐ๋” ๊ฐœ๋…๋ฌธ์ œ๋ฅผ ์„ ์ •ํ•˜์˜€๋‹ค. ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ๋‹ค์–‘ํ•œ ์šด๋™ ์กฐ๊ฑด์— ๋”ฐ๋ฅธ ํ•ด์„๊ฒฐ๊ณผ๊ฐ€ ์ด๋ก ํ•ด๋ฅผ ์ ์ ˆํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๋Š”์ง€ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, 3์ฐจ์› ์Šฌ๋ž˜๋ธŒ๋ฅผ ๋‹ค์–‘ํ•œ ์™ธ๋ ฅ์ด ๋ณตํ•ฉ์ ์œผ๋กœ ์ž‘์šฉํ•˜๋Š” ์šด๋™ ์กฐ๊ฑด์„ ์ ์šฉํ•˜์—ฌ Motion model์˜ MULTID ์ปดํฌ๋„ŒํŠธ ํ•ด์„๋Šฅ๋ ฅ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰ ์„ธ ๋ฒˆ์งธ๋กœ ๊ฐœ์„ ๋œ ์ฝ”๋“œ๋ฅผ ์—ด์ˆ˜๋ ฅ ํ˜„์ƒ ์˜ˆ์ธก์— ํ™œ์šฉํ•˜์—ฌ ์šด๋™ ์กฐ๊ฑด์—์„œ ์œ ๋™ ๋ถˆ์•ˆ์ •์„ฑ(Flow instability)๊ณผ ์ž„๊ณ„์—ด์œ ์†(Critical Heat Flux)์˜ ๋ณ€ํ™” ๊ฑฐ๋™์„ ๊ฒ€ํ† ํ•˜์˜€๋‹ค. ๋จผ์ €, RELAP5 ์ฝ”๋“œ๋ฅผ ํ†ตํ•ด 2์ƒ ์กฐ๊ฑด์—์„œ ์œ ๋™ ๋ถˆ์•ˆ์ •์„ฑ์„ ์˜ˆ์ธกํ•œ ๊ฒฐ๊ณผ(M. Colombo et al., 2012)๋ฅผ MARS-KS ํ•ด์„๊ฒฐ๊ณผ์™€ ๋น„๊ตํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ MARS-KS motion model์„ ํ†ตํ•ด ์ •์ง€ ์ƒํƒœ์™€ ์šด๋™ ์กฐ๊ฑด์—์„œ ์œ ๋™ ๋ถˆ์•ˆ์ •์„ฑ์œผ๋กœ ์ธํ•ด ๋‚˜ํƒ€๋‚˜๋Š” ๋ถˆ์•ˆ์ • ์˜์—ญ(Unstable region)์„ ์˜ˆ์ธกยท๋น„๊ตํ•˜์˜€๋‹ค. ์œ ๋™ ๋ถˆ์•ˆ์ •์„ฑ์— ์ด์–ด, ๋™์ผํ•œ ํ•ด์„๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐ™์€ ์—ด์† ์กฐ๊ฑด์—์„œ ๋™์ ์šด๋™์— ์˜ํ•œ ์ž„๊ณ„์—ด์œ ์†์˜ ๋ณ€ํ™”๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ž„๊ณ„์—ด์œ ์†์ด ๋ฐœ์ƒํ•˜๋ฉด ์ž‘๋™ ์œ ์ฒด์— ๋Œ€ํ•œ ์—ด์ „๋‹ฌ ํšจ์œจ์ด ํฌ๊ฒŒ ๊ฐ์†Œํ•˜์—ฌ ๋ฒฝ๋ฉด ์˜จ๋„๊ฐ€ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ •์ง€ ์ƒํƒœ์™€ ์šด๋™ ์กฐ๊ฑด์—์„œ ๋ฒฝ๋ฉด ์˜จ๋„๋ฅผ ๋น„๊ตํ•˜์—ฌ ์ž„๊ณ„์—ด์œ ์†์˜ ๋ฐœ์ƒ ์—ฌ๋ถ€๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ด์™€ ๊ฐ™์€ ์‘์šฉ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ฐœ์„ ๋œ ์ฝ”๋“œ์˜ ํ™œ์šฉ์„ฑ์„ ์ž…์ฆํ•˜์˜€๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ ๋ฒ”์œ„ 4 ์ œ 2 ์žฅ MARS-KS motion model ๊ฐœ์„ ์—ฐ๊ตฌ 6 ์ œ 1 ์ ˆ Motion model ๊ฐœ์š” 6 ์ œ 2 ์ ˆ Motion model ๊ฐœ์„ ์—ฐ๊ตฌ ํ˜„ํ™ฉ 8 ์ œ 3 ์ ˆ Motion model ๊ฐœ์„  13 1. Volume direction unit vector ์ƒ์„ฑ๋ฐฉ๋ฒ• ๊ฐœ์„  13 2. ๊ด€์„ฑ๋ ฅ์„ ๊ณ ๋ คํ•œ ์ง์„ ๊ฐ€์†๋„ ํ•ญ ์ˆ˜์ • 16 3. ํšŒ์ „ ์ค‘์‹ฌ์— ๊ด€ํ•œ ์˜ค๋ฅ˜ ์ˆ˜์ • 17 4. ์ •์…˜ ์ •๋ณด์˜ ์—…๋ฐ์ดํŠธ 18 5. ๋‹ค์ฐจ์› ์œ ๋™ ํ•ด์„๋Šฅ๋ ฅ ๊ตฌํ˜„ 19 6. Motion model์˜ MULTID ์ปดํฌ๋„ŒํŠธ ํ™•์žฅ 23 ์ œ 3 ์žฅ MARS-KS motion model ๊ฒ€์ฆ์—ฐ๊ตฌ 37 ์ œ 1 ์ ˆ 1D ์ปดํฌ๋„ŒํŠธ ๊ฒ€์ฆ 37 1. ๋งˆ๋…ธ๋ฏธํ„ฐ์˜ ์šด๋™ 38 2. ์ˆ˜์ง ํŒŒ์ดํ”„์˜ ์ˆ˜์ง์š”๋™ 41 ์ œ 2 ์ ˆ ๊ต์ฐจ์œ ๋™ ๊ฒ€์ฆ 43 1. 4๊ฐœ ํŒŒ์ดํ”„์˜ ์šด๋™ 44 2. 6๊ฐœ ํŒŒ์ดํ”„์˜ ์šด๋™ 45 3. 8๊ฐœ ํŒŒ์ดํ”„์˜ ์šด๋™ 45 ์ œ 3 ์ ˆ MULTID ์ปดํฌ๋„ŒํŠธ ๊ฒ€์ฆ 47 1. 3์ฐจ์› ์Šฌ๋ž˜๋ธŒ์˜ ์šด๋™ 47 2. 3์ฐจ์› ์‹ค๋ฆฐ๋”์˜ ์šด๋™ 52 3. MULTID ์ปดํฌ๋„ŒํŠธ ํ•ด์„๋Šฅ๋ ฅ ํ™•์ธ 55 ์ œ 4 ์žฅ MARS-KS motion model ์‘์šฉ์—ฐ๊ตฌ 82 ์ œ 1 ์ ˆ Motion model์„ ํ†ตํ•œ ์œ ๋™ ๋ถˆ์•ˆ์ •์„ฑ ์˜ˆ์ธก 83 ์ œ 2 ์ ˆ Motion model์„ ํ†ตํ•œ ์ž„๊ณ„์—ด์œ ์† ์˜ˆ์ธก 87 ์ œ 5 ์žฅ ๊ฒฐ๋ก  99 ์šฉ์–ด์„ค๋ช… 101 ์ฐธ๊ณ ๋ฌธํ—Œ 103 ๋ถ€๋ก 1. MARS-KS motion model ๋งค๋‰ด์–ผ 106 Abstract 110Maste

    Organization of the 5' region of the rat ATP-citrate lyase gene

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    A genomic clone, encompassing the 5' flanking region and the first seven exons of rat ATP citrate lyase gene, was isolated from a rat genomic library and sequenced. Primer-extension analysis showed that mRNA is transcribed at 4407 nucleotides upstream from the translation start site. Primer-extension analysis and sequencing of ATP citrate lyase cDNA amplified by PCR showed that the promoter used for transcription is identical in mammary gland, lung, liver, brain and kidney. Southern-blot analysis showed that the ATP citrate lyase gene exists as a single copy. The 5' flanking region contains several consensus sequences defined as promoter elements. These include a CAAT box and Sp1-binding sites. However, a TATA box lacks this promoter. The expression of the chloramphenicol acetyltransferase gene was induced by the 5' flanking region (-2370 to -1) in the CHO cell line. The 5' flanking region also contains several sequence elements that may be involved in the transcriptional regulation of the gene.ope

    Secreted PCSK9 decreases the number of LDL receptors in hepatocytes and inlivers of parabiotic mice

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    Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a member of the proteinase K subfamily of subtilases that reduces the number of LDL receptors (LDLRs) in liver through an undefined posttranscriptional mechanism. We show that purified PCSK9 added to the medium of HepG2 cells reduces the number of cell-surface LDLRs in a dose- and time-dependent manner. This activity was approximately 10-fold greater for a gain-of-function mutant, PCSK9(D374Y), that causes hypercholesterolemia. Binding and uptake of PCSK9 were largely dependent on the presence of LDLRs. Coimmunoprecipitation and ligand blotting studies indicated that PCSK9 and LDLR directly associate; both proteins colocalized to late endocytic compartments. Purified PCSK9 had no effect on cell-surface LDLRs in hepatocytes lacking autosomal recessive hypercholesterolemia (ARH), an adaptor protein required for endocytosis of the receptor. Transgenic mice overexpressing human PCSK9 in liver secreted large amounts of the protein into plasma, which increased plasma LDL cholesterol concentrations to levels similar to those of LDLR-knockout mice. To determine whether PCSK9 was active in plasma, transgenic PCSK9 mice were parabiosed with wild-type littermates. After parabiosis, secreted PCSK9 was transferred to the circulation of wild-type mice and reduced the number of hepatic LDLRs to nearly undetectable levels. We conclude that secreted PCSK9 associates with the LDLR and reduces hepatic LDLR protein levels.ope

    Cyclase-associated Protein 1 Is a Binding Partner of Proprotein Convertase Subtilisin/Kexin type-9 and Is Required for the Degradation of Low-Density Lipoprotein Receptors by Proprotein Convertase Subtilisin/Kexin type-9

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    Aims: Proprotein convertase subtilisin/kexin type-9 (PCSK9), a molecular determinant of low-density lipoprotein (LDL) receptor (LDLR) fate, has emerged as a promising therapeutic target for atherosclerotic cardiovascular diseases. However, the precise mechanism by which PCSK9 regulates the internalization and lysosomal degradation of LDLR is unknown. Recently, we identified adenylyl cyclase-associated protein 1 (CAP1) as a receptor for human resistin whose globular C-terminus is structurally similar to the C-terminal cysteine-rich domain (CRD) of PCSK9. Herein, we investigated the role of CAP1 in PCSK9-mediated lysosomal degradation of LDLR and plasma LDL cholesterol (LDL-C) levels. Methods and results: The direct binding between PCSK9 and CAP1 was confirmed by immunoprecipitation assay, far-western blot, biomolecular fluorescence complementation, and surface plasmon resonance assay. Fine mapping revealed that the CRD of PCSK9 binds with the Src homology 3 binding domain (SH3BD) of CAP1. Two loss-of-function polymorphisms found in human PCSK9 (S668R and G670E in CRD) were attributed to a defective interaction with CAP1. siRNA against CAP1 reduced the PCSK9-mediated degradation of LDLR in vitro. We generated CAP1 knock-out mice and found that the viable heterozygous CAP1 knock-out mice had higher protein levels of LDLR and lower LDL-C levels in the liver and plasma, respectively, than the control mice. Mechanistic analysis revealed that PCSK9-induced endocytosis and lysosomal degradation of LDLR were mediated by caveolin but not by clathrin, and they were dependent on binding between CAP1 and caveolin-1. Conclusion: We identified CAP1 as a new binding partner of PCSK9 and a key mediator of caveolae-dependent endocytosis and lysosomal degradation of LDLR.ope

    The roles of sterol regulatory element-binding proteins in the transactivation of the rat ATP citrate-lyase promoter

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    ATP citrate-lyase (ACL) is a key enzyme supplying acetyl-CoA for fatty acid and cholesterol synthesis. Its expression is drastically up-regulated when an animal is fed a low fat, high carbohydrate diet after prolonged fasting. In this report, we describe the role of sterol regulatory element-binding proteins (SREBPs) in the transactivation of the rat ACL promoter. ACL promoter activity was markedly stimulated by the overexpression of SREBP-1a and, to a lesser extent, by SREBP-2 in Alexander human hepatoma cells. The promoter elements responsive to SREBPs were located within the 55-base pair sequences from -114 to -60. The gel mobility shift assay revealed four SREBP-1a binding sites in this region. Of these four elements, the -102/-94 region, immediately upstream of the inverted Y-box, and the -70/-61 region, just adjacent to Sp1 binding site, played critical roles in SREBPs-mediated stimulation. The mutation in the inverted Y-box and the coexpression of dominant negative nuclear factor-Y (NF-Y) significantly attenuated the transactivation by SREBP-1a, suggesting that NF-Y binding is a prerequisite for SREBPs to activate the ACL promoter. However, the multiple Sp1 binding sites did not affect the transactivation of the ACL promoter by SREBPs. The binding affinity of SREBP-1a to SREs of the ACL promoter also was much higher than that of SREBP-2. The transactivation potencies of the chimeric SREBPs, of which the activation domains (70 amino acids of the amino terminus) were derived from the different species of their carboxyl-terminal region, were similar to those of SREBPs corresponding to their carboxyl termini. Therefore, it is suggested that the carboxyl-terminal portions of SREBPs containing DNA binding domains are important in determining their transactivation potencies to a certain promoter.ope
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