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1 March 2007 A Fully Automated Procedure for the High-Throughput Detection of Avian Influenza Virus by Real-Time Reverse Transcription–Polymerase Chain Reaction
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Abstract

The recent spread of highly pathogenic H5N1 avian influenza (AI) has made it important to develop highly sensitive diagnostic systems for the rapid detection of AI genome and the differentiation of H5N1 variants in a high number of samples. In the present paper, we describe a high-throughput procedure that combines automated extraction, amplification, and detection of AI RNA, by an already described TaqMan real-time reverse transcription–polymerase chain reaction (RRT-PCR) assay targeted at the matrix (M) protein gene of AI virus (AIV). The method was tested in cloacal and tracheal swabs, the most common type of samples used in AI surveillance, as well as in tissue and fecal samples. A robotic system (QIAGEN Biosprint 96) extracted RNA and set up reactions for RRT-PCR in a 96-well format. The recovery of the extracted RNA was as efficient as that of a manual RNA extraction kit, and the sensitivity of the detection system was as high as with previously described nonautomated methods. A system with a basic configuration (one extraction robot plus two real-time 96-well thermocyclers) operated by two persons could account for about 360 samples in 5 hr. Further characterization of AI RNA–positive samples with a TaqMan RRT-PCR specific for H5 (also described here) and/or N1 was possible within 2 hr more. As this work shows, the system can analyze up to 1400 samples per working day by using two nucleic acid extraction robots and a 384-well-format thermocycler.

Montserrat Agüero, Elena San Miguel, Azucena Sánchez, Concepción Gómez-Tejedor, and Miguel Angel Jiménez-Clavero "A Fully Automated Procedure for the High-Throughput Detection of Avian Influenza Virus by Real-Time Reverse Transcription–Polymerase Chain Reaction," Avian Diseases 51(s1), 235-241, (1 March 2007). https://doi.org/10.1637/7634-042806R1.1
Received: 28 April 2006; Accepted: 1 July 2006; Published: 1 March 2007
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