Abstract

Prostate cancer is the most common cancer among men beyond 50 years old, and ranked the second in mortality. The level of Prostate-specific antigen (PSA) in serum has been a routine biomarker for clinical assessment of the cancer development, which is detected mostly by antibody-based immunoassays. The proteolytic activity of PSA also has important functions. Here a genetically encoded biosensor based on fluorescence resonance energy transfer (FRET) technology was developed to measure PSA activity. In vitro assay showed that the biosensor containing a substrate peptide ‘RLSSYYSGAG’ had 400% FRET change in response to 1 µg/ml PSA within 90 min, and could detect PSA activity at 25 ng/ml. PSA didn’t show enzymatic activity toward the biosensor in serum solution, likely reflecting the existence of other inhibitory factors besides Zn2+. By expressing the biosensor on cell plasma membrane, the FRET responses were significant, but couldn’t distinguish well the cultured prostate cancer cells from non-prostate cancer cells under microscopy imaging, indicating insufficient speci- ficity to PSA. The biosensor with the previously known ‘HSSKLQ’ substrate showed little response to PSA in solution. In summary, we developed a genetically encoded FRET biosensor to detect PSA activity, which may serve as a useful tool for relevant applications, such as screening PSA activation substrates or inhibitors; the purified biosensor protein can also be an alternative choice for measuring PSA activity besides currently commercialized Mu-HSSKLQ-AMC substrate from chemical synthesis.

Details

Title
Genetically Encoded FRET Biosensor Detects the Enzymatic Activity of Prostate-Specific Antigen
Author
Yao, Hui; Wang, Liqun; Guo, Jia; Liu, Weimin; Li, Jingjing; Wang, Yingxiao; Deng, Linhong; Ouyang, Mingxing
Pages
101-111
Section
ARTICLE
Publication year
2020
Publication date
2020
Publisher
Tech Science Press
ISSN
15565297
e-ISSN
15565300
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2419202064
Copyright
© 2020. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.