Novel Estrogen Receptor Dimerization BRET-Based Biosensors for Screening Estrogenic Endocrine-Disrupting Chemicals

Author:

Choi Gyuho1,Kang Hyunkoo1,Suh Jung-Soo1,Lee Haksoo1,Han Kiseok1,Yoo Gaeun1,Jo Hyejin2,Shin Yeong Min2,Kim Tae-Jin1345,Youn BuHyun135ORCID

Affiliation:

1. Department of Integrated Biological Science, Pusan National University, Busan 46241, Republic of Korea.

2. Food Safety Risk Assessment Division, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju 28159, Republic of Korea.

3. Department of Biological Sciences, Pusan National University, Busan 46241, Republic of Korea.

4. Institute of Systems Biology, Pusan National University, Busan 46241, Republic of Korea.

5. Nuclear Science Research Institute, Pusan National University, Busan 46241, Republic of Korea.

Abstract

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.

Funder

Ministry of Food and Drug Safety

Publisher

American Association for the Advancement of Science (AAAS)

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