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Published on: July 22, 2014
A novel viral RNA detection method based on the combined use of trans-acting ribozymes and HCR-FRET analyses
Leonardo Ferreira da Silva1, Aisel Valle Garay2, Pedro Felipe Queiroz1
1Laboratory of Synthetic Biology, Department of Genetics and Morphology, Institute of Biological Science, University of Brasília (UnB), Brasília, Federal District, Brazil.
Abstract:
The diagnoses of retroviruses are essential for controlling the rapid spread of pandemics. However, the real-time Reverse Transcriptase quantitative Polymerase Chain Reaction (RT-qPCR), which has been the gold standard for identifying viruses such as SARS-CoV-2 in the early stages of infection, is associated with high costs and logistical challenges. To innovate in viral RNA detection a novel molecular approach for detecting SARS-CoV-2 viral RNA, as a proof of concept, was developed. This method combines specific viral gene analysis, trans-acting ribozymes, and Fluorescence Resonance Energy Transfer (FRET)-based hybridization of fluorescent DNA hairpins. In this molecular mechanism, SARS-CoV-2 RNA is specifically recognized and cleaved by ribozymes, releasing an initiator fragment that triggers a hybridization chain reaction (HCR) with DNA hairpins containing fluorophores, leading to a FRET process. A consensus SARS-CoV-2 RNA target sequence was identified, and specific ribozymes were designed and transcribed in vitro to cleave the viral RNA into fragments. DNA hairpins labeled with Cy3/Cy5 fluorophores were then designed and synthesized for HCR-FRET assays targeting the RNA fragment sequences resulting from ribozyme cleavage. The results demonstrated that two of the three designed ribozymes effectively cleaved the target RNA within 10 minutes. Additionally, DNA hairpins labeled with Cy3/Cy5 pairs efficiently detected target RNA specifically and triggered detectable HCR-FRET reactions. This method is versatile and can be adapted for use with other viruses. Furthermore, the design and construction of a DIY photo-fluorometer prototype enabled us to explore the development of a simple and cost-effective point-of-care detection method based on digital image analysis.
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