A thermophilic Cas13 enzyme for sensitive and specific one-pot detection of SARS-CoV-2
Masson, Mauricio Lopez Portillo
Mahfouz, Magdy M.
KAUST DepartmentBioscience Program
Biological and Environmental Science and Engineering (BESE) Division
King Abdullah University of Science and Technology
Visual Computing Center (VCC)
Electrical and Computer Engineering Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Desert Agriculture Initiative
Permanent link to this recordhttp://hdl.handle.net/10754/672104
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AbstractRobust, sensitive, and specific diagnostic platforms for early pathogen detection are essential for the identification of infected patients and management of current and future pandemics. CRISPR-Cas systems have been repurposed for SARS-CoV-2 detection in two-pot assays. Two-pot assays require extra steps and are prone to cross-contamination; however, the temperature range of current Cas enzymes limits the development of one-pot assays Here, we characterized TccCas13a, a thermophilic Cas13a enzyme with cis and trans activities from 37–70°C, and HheCas13a, which had a limited range and lower activity. We harnessed TccCas13a in a one-pot SARS-CoV-2 assay with two layers of amplification and TccCas13a-mediated collateral degradation of a single-stranded RNA reporter. This assay showed 95% sensitivity and 100% specificity compared with RT-qPCR on clinical samples. We also developed a mobile phone application to facilitate data reading, collection, and sharing. Our OPTIMA-dx detection module exhibits key features for point-of-care SARS-CoV-2 screening and pathogen detection in general.
CitationMahas, A., Marsic, T., Masson, M., Wang, Q., Aman, R., Zheng, C., … Mahfouz, M. (2021). A thermophilic Cas13 enzyme for sensitive and specific one-pot detection of SARS-CoV-2. doi:10.21203/rs.3.rs-604849/v1
SponsorsWe would like to thank Mr. Mohammad Alarawi for providing the RNA of SARS-CoV-2 clinical samples.We thank members of the genome engineering and synthetic biology laboratory for insightful discussions and technical support. This work was supported, in part, by the Smart Health Initiative at KAUST and the IAF and NTGC grants from the KAUST IED to MM.
PublisherResearch Square Platform LLC
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