基于RPA CRISPR单碱基识别系统鉴定鸭甲型肝炎病毒3强毒株和减毒株的检测方法

IF 3.8 1区农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE

Poultry Science Pub Date : 2025-05-01 DOI:10.1016/j.psj.2025.105241

Lei Chen , Qiaoli Zhang , Wenbo Sun , Michael G. Mauk , Qingmei Li

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引用次数: 0

摘要

鸭病毒性肝炎在中国非常常见，给养鸭业造成了重大影响和经济损失。目前，DHAV-3已成为中国鸭病毒性肝炎的主要致病因子。然而，现有的DHAV-3疫苗不能完全排除疫苗株毒性增强的潜在风险。由于野生菌株和疫苗菌株之间的基因组序列高度相似（只有几个碱基差异），传统的检测方法难以准确区分它们，严重干扰疾病控制决策。因此，同时检测DHAV-3毒株和弱毒株对于评估疫苗效力、监测病毒突变和优化控制策略至关重要。本研究以DHAV-3 SD70减毒株为例，建立了一种高灵敏度、快速的检测方法来鉴别和区分DHAV-3毒株和SD70减毒株，为鉴别这两种毒株提供了一种新的策略。目前尚无文献报道这两种菌株的检测方法。因此，我们提出了一种基于rna - crispr的单碱基识别系统策略。该方法仅根据几个不同的碱基序列特异性地鉴定出DHAV-3毒株和弱毒株。该方法可在35 min内检测到低至10°拷贝/μL的两个目标基因。此外，将该方法用于样品分析时，将该方法的结果与测序结果、公司提供的结果进行比较，发现结果一致。该方法具有速度快、操作简单、特异性和灵敏度高的优点，可用于DHAV-3毒力株和SD70减毒株的检测，为疾病控制、疫苗评价和突变监测奠定了技术基础。

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

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.

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来源期刊

Poultry Science 农林科学-奶制品与动物科学

CiteScore

7.60

自引率

15.90%

发文量

审稿时长

94 days

期刊介绍： First self-published in 1921, Poultry Science is an internationally renowned monthly journal, known as the authoritative source for a broad range of poultry information and high-caliber research. The journal plays a pivotal role in the dissemination of preeminent poultry-related knowledge across all disciplines. As of January 2020, Poultry Science will become an Open Access journal with no subscription charges, meaning authors who publish here can make their research immediately, permanently, and freely accessible worldwide while retaining copyright to their work. Papers submitted for publication after October 1, 2019 will be published as Open Access papers. An international journal, Poultry Science publishes original papers, research notes, symposium papers, and reviews of basic science as applied to poultry. This authoritative source of poultry information is consistently ranked by ISI Impact Factor as one of the top 10 agriculture, dairy and animal science journals to deliver high-caliber research. Currently it is the highest-ranked (by Impact Factor and Eigenfactor) journal dedicated to publishing poultry research. Subject areas include breeding, genetics, education, production, management, environment, health, behavior, welfare, immunology, molecular biology, metabolism, nutrition, physiology, reproduction, processing, and products.