Development and application of a novel triplex protein microarray method for rapid detection of antibodies against avian influenza virus, Newcastle disease virus, and avian infectious bronchitis virus

ARCHIVES OF VIROLOGY(2021)

引用 3|浏览6
暂无评分
摘要
Avian influenza virus (AIV), Newcastle disease virus (NDV), and avian infectious bronchitis virus (IBV) inflict immense damage on the global poultry industry annually. Serological diagnostic methods are fundamental for the effective control and prevention of outbreaks caused by these viruses. In this study, a novel triplex protein microarray assay was developed and validated for the rapid and simultaneous visualized detection of antibodies against AIV, NDV, and IBV in chicken sera. The AIV nuclear protein (NP), NDV phosphoprotein (P), and IBV nonstructural protein 5 (nsp5) were produced in a prokaryotic expression system, purified, and immobilized onto an initiator integrated poly(dimethylsiloxane) (iPDMS) film as probes to detect antibodies against these viruses in chicken sera. After optimization of the reaction conditions, no cross-reactivity was detected with infectious bursal disease virus, avian leukosis virus subgroup J and chicken anemia virus antisera. The lowest detectable antibody titers in this assay corresponded to hemagglutination inhibition (HI) titers of 2 4 and 2 1 for AIV and NDV, respectively, and to an IDEXX antibody titer of 10 3 for IBV, using the HI assay and IDEXX commercial ELISA kit as the reference methods. When156 serum samples were tested using the new assay, the HI test and the IBV IDEXX ELISA kit, the assay showed 96.8% (151/156), 97.4% (152/156) and 99.4% (155/156) diagnostic accuracy for detection of AIV, NDV and IBV antibody, respectively. The current study suggests that the newly developed triplex microarray is rapid, sensitive, and specific, providing a viable alternative assay for AIV, NDV, and IBV antibody screening in epidemiological investigations and vaccination evaluations.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要