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논문 기본 정보

자료유형
학술저널
저자정보
Gyuho Choi (Department of Integrated Biological Science, Pusan National University) Hyunkoo Kang (Department of Integrated Biological Science, Pusan National University) Jung-Soo Suh (Department of Integrated Biological Science, Pusan National University) Haksoo Lee (Department of Integrated Biological Science, Pusan National University) Kiseok Han (Department of Integrated Biological Science, Pusan National University) Gaeun Yoo (Department of Integrated Biological Science, Pusan National University) Hyejin Jo (Food Safety Risk Assessment Division, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety) 신영민 (식품의약품안전청) 김태진 (부산대학교) BuHyun Youn (Pusan National University)
저널정보
한국생체재료학회 생체재료학회지 Biomaterials Research Vol.28
발행연도
2024.3
수록면
100 - 114 (15page)
DOI
10.34133/bmr.0010

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초록· 키워드

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The increasing prevalence of endocrine-disrupting chemicals (EDCs) in our environment is a growing concern, with numerous studies highlighting their adverse effects on the human endocrine system. Among the EDCs, estrogenic endocrine-disrupting chemicals (eEDCs) are exogenous compounds that perturb estrogenic hormone function by interfering with estrogen receptor (ER) homo (α/α, β/β) or hetero (α/β) dimerization. To date, a comprehensive screening approach for eEDCs affecting all ER dimer forms in live cells is lacking. Here, we developed ER dimerization-detecting biosensors (ERDDBs), based on bioluminescence resonance energy transfer, for dimerization detection and rapid eEDC identification. To enhance the performance of these biosensors, we determined optimal donor and acceptor locations using computational analysis. Additionally, employing HaloTag as the acceptor and incorporating the P2A peptide as a linker yielded the highest sensitivity among the prototypes. We also established stable cell lines to screen potential ER dimerization inducers among estrogen analogs (EAs). The EAs were categorized through cross-comparison of ER dimer responses, utilizing EC values derived from a standard curve established with 17β-estradiol. We successfully classified 26 of 72 EAs, identifying which ER dimerization types they induce. Overall, our study underscores the effectiveness of the optimized ERDDB for detecting ER dimerization and its applicability in screening and identifying eEDCs.

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