A multiplex real-time RT-PCR for simultaneous detection of four most common avian respiratory viruses

Highlights: • We present a novel multiplex qRT-PCR assay for detection and quantification of avian respiratory viruses. • The optimal cutoff point, the corresponding LoQ and LoD were statistically established based on ROC curves analysis. • The developed multiplex assay is superior to other commonly used methods because it is rapid, reproducible, highly specific and sensitive. A one-step multiplex real-time reverse transcription-PCR (rRT-PCR) assay was developed for simultaneous detection and quantification of four avian respiratory viruses: avian influenza virus (AIV), infectious bronchitis virus (IBV), Newcastle disease virus (NDV) and infectious laryngotracheitis virus (ILTV). In comparison with the singleplex rRT-PCR, the specificity, the sensitivity and the reproducibility of the new assay were evaluated and validated using 70 clinical samples. The optimal cutoff point, the corresponding limit of quantification (LoQ) and the limit of detection (LoD) were statistical established based on receiver operating characteristic (ROC) curve analysis. The results showed that the multiplex assay presents higher sensitivity and specificity. Correlation coefficients (R²) and amplification efficiencies (E) of all singleplex and multiplex rRT-PCR reactions are within the acceptable range. The 95% LoDs of multiplex assay were in the range [3–19] copies genomic/ µl, and its corresponding cutoff cycles were in the range [34.16–36.59]. No competitive inhibition for the detection of the four targets and no specific amplification or cross reactivity with other tested viruses was observed. Excellent results were attained in the inter-assay and intra-assay reproducibility evaluation. All identified samples by the multiplex rRT-PCR assay proved to be 100% concordant with the results of the singleplex assays. The results achieved showed that the multiplex assay is very suitable as a routine laboratory test for rapid and specific detection and quantification of co-infections in field samples.