Detection method for identifying duck hepatitis A virus 3 virulent and attenuated strains based on RPA CRISPR single-base recognition system.

Abstract

Duck viral hepatitis is very common in China, causing significant impact and economic losses to the duck farming industry. Currently, DHAV-3 has become the main factor causing duck viral hepatitis in China. However, the existing DHAV-3 vaccines cannot completely rule out the potential risk of the vaccine strain becoming more virulent. Due to the high similarity in genomic sequences between wild strains and vaccine strains (with only a few base differences), traditional detection methods struggle to accurately differentiate them, severely interfering with disease control decisions. Therefore, simultaneously detecting both the virulent strain and the attenuated strain of DHAV-3 is crucial for evaluating vaccine efficacy, monitoring virus mutations, and optimizing control strategies. This study, using the DHAV-3 SD70 attenuated strain as an example, developed a highly sensitive and rapid detection method to identify and distinguish between the DHAV-3 virulent and SD70 attenuated strains, providing a new strategy for identifying both strains. Currently, there are no literature reports on the detection methods for the two strains. Therefore, we propose a single-base recognition system strategy based on RPA-CRISPR. DHAV-3 virulent and attenuated strains were specifically identified by this method based on only a few different base sequences. This method can detect two target genes as low as 10° copy/μL within 35 min. In addition, when this method was used for samples analysis, the results of this method, sequencing results, and the results provided by the company were compared and found to be consistent. This method has the advantages of fast speed, simple operation, high specificity and sensitivity, which can be used for the detection of DHAV-3 virulence strain and SD70 attenuated strain, and lays a technical foundation for disease control, vaccine evaluation and mutation monitoring.