A multiplex real-time reverse transcription polymerase chain reaction assay for differentiation of classical and variant II strains of avian infectious bronchitis virus

Abstract

Identification of avian infectious bronchitis virus (IBV) genotypes is essential for controlling infectious bronchitis (IB) disease, because vaccines that differ from the circulating strains might not provide efficient cross-protection. In Egypt, IBV strain typing is a difficult process, due to the widespread distribution of four genotype lineages (GI-13, GI-23, GI-1, and GI-16), which may contribute to IBV vaccination failure. In this study, we developed a multiplex real-time quantitative reverse transcription polymerase chain reaction (mRT-qPCR) assay that targets highly conserved areas of the S1 gene in order to detect classical (G1) and Egyptian variant II (G23) strains in allantoic fluids and clinical samples. The viral genotyping technique was assessed using commercially available vaccines as well as local strains, and 16 field isolates were tested to investigate its clinical applicability. The assay was found to be specific for the detection of classical and VAR II strains and did not detect the VAR I strain or other avian pathogens such as Newcastle disease virus, avian influenza virus (H9N2 and H5N8), or infectious bursal disease virus. The results also showed that 28 out of 41 samples tested positive for IBV utilizing rt-qRT-PCR targeting the N gene and that 26 out of the 28 positive samples were genotyped by mRT-qPCR targeting the S1 gene, whereas the remaining two samples that were not genotyped were VAR 1 (4/91) and VAR I (793/B). Interestingly, the testing could identify combined infections in one sample, indicating a mixed infection with both genotypes. The real-time RT-PCR assay could detect viral RNA at concentrations as low as 10² EID₅₀ /ml for both classical and variant II. This assay is rapid, specific, and sensitive. It appears to be a valuable tool for regular disease monitoring that can be used to differentiate as well as identify viruses.