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Universal Exponential Amplification Confers Multilocus Detection of Mutation-Prone Virus
Yue Zhao1, Xiaoxing Fang1, Huahang Yu1
1Institute of Analytical Chemistry and Instrument for Life Science, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xianning West Road, Xi'an, Shaanxi 710049, P. R. China.
Analytical Chemistry
|January 5, 2022
Summary
Accurate COVID-19 detection is crucial. A new multilocus method targets multiple SARS-CoV-2 genes, improving diagnostic accuracy and overcoming limitations of single-locus tests for infectious diseases.
Area of Science:
- Molecular Biology
- Virology
- Diagnostic Development
Background:
- The COVID-19 pandemic necessitates accurate and scalable diagnostic tools.
- Current single-locus SARS-CoV-2 detection methods are prone to false negatives due to viral mutations.
- RNA virus mutation rates pose challenges for reliable infectious disease diagnostics.
Purpose of the Study:
- To develop a multilocus detection method for SARS-CoV-2.
- To enhance diagnostic accuracy and reliability by targeting multiple viral genes.
- To create a versatile platform for detecting mutation-prone RNA viruses.
Main Methods:
- Modified loop-mediated isothermal amplification (mLAMP) with universal primers.
- Sequence-specific probes targeting SARS-CoV-2 nucleocapsid (N) and Orf1ab genes.
- Multiplex real-time analysis using flap structure-dependent endonuclease and multicolor TaqMan probes.
Main Results:
- Achieved a limit of detection as low as 250 aM for synthetic target RNA.
- Validated the feasibility of multilocus detection using mutation-prone genes.
- Demonstrated improved accuracy compared to single-locus methods.
Conclusions:
- The developed multilocus method offers a significant advancement for accurate SARS-CoV-2 analysis.
- This approach holds great promise for clinical diagnosis of COVID-19 and other infectious diseases.
- Multilocus detection is a robust strategy for combating diagnostics challenges posed by rapidly mutating viruses.
