| Discovery of antigens for early detection of Mycobacterium avium subsp. paratuberculosis and analysis of characteristics using bioinformatics tools |
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Hong-Tae Park1, Hyun-Eui Park1, Min-Kyoung Shin1, Yong-Il Cho2, Han Sang Yoo1 |
1Department of Infectious Diseases, College of Veterinary Medicine, Seoul National University 2Department of Animal Resources Development, National Institute of Animal Science, Rural Development Administration |
| Mycobacterium avium subsp. paratuberculosis 감염 초기 개체 검출을 위한 항원 탐색 및 특성 분석 |
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박홍태1, 박현의1, 신민경1, 조용일2, 유한상1 |
1서울대학교 수의과대학 2농촌진흥청 국립축산과학원 |
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| Abstract |
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Johne's disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP), is one of the most widespread and economically important diseases in cattle. Current diagnostic methods are based on the detection of anti-MAP antibodies in serum or isolation of the causative agent. However, these techniques are often not applicable for cases of subclinical infection due to relatively low sensitivity. Therefore, finding new antigen candidates that strongly react with the host immune system had been attempted. To effectively detect infection during the subclinical stage, several antigen candidates were selected based on previous researches. Characteristics of the selected antigen candidates were analyzed using bioinformatics-based prediction tools. A total of nine antigens were selected (MAP0862, MAP3817c, MAP2077c, MAP0860c, MAP3954, MAP3155c, MAP1204, MAP1087, and MAP2963c) that have MAP-specific and/or high immune responses to infected animals. Using a transmembrane prediction tool, five of the nine antigen candidates were predicted to be membrane protein (MAP3817c, MAP3954, MAP3155c, MAP1087, and MAP1204). Some of the predicted protein structures identified using the I-TASSER server shared similarities with known proteins found in the Protein Data Bank database (MAP0862, MAP1204, and MAP2077c). In future studies, the characteristics and diagnostic efficiency of the selected antigen candidates will be evaluated. |
| Key Words:
antigen, bioinformatics, discovery, Johne's disease |
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