281151 Application of a Novel Tablet PCR Platform for Detection of Influenza Subtypes From Clinical Samples

Thursday, November 1, 2012: 1:24 PM
Crawford East (Westin )
Stephanie Angione1, Zintis Inde2, Christina Beck3, Steve M. Opal4, Andrew W. Artenstein4 and Anubhav Tripathi3, (1)Center for Biomedical Engineering, School of Engineering, Brown University, Providence, RI, (2)Department of Biology and Medicine, Brown University, Providence, RI, (3)School of Engineering, Brown University, Providence, RI, (4)Brown Medical School, Memorial Hospital of RI, Pawtucket, RI

  Developing diagnostics which can quickly and effectively identify viral infections ranging from HIV to hepatitis is crucially important for addressing the global health impact of these diseases. This is particularly true in the case of influenza, a disease which not only spreads quickly, but also mutates rapidly. The gold standard approach for influenza subtyping is RT-PCR (qRT-PCR) which allows for detection of the target sequence and quantification the number of viral particles present. Because RT-PCR and qRT-PCR require thermal cycling in order to amplify the clinical sample for detection, a typical apparatus for these techniques is costly, bulky, and requires a large sample volume. Thus, a microfluidic approach may prove advantageous in addressing these issues. Thus, we present our work on a droplet platform for the differentiation of influenza strains using qRT-PCR using a novel microdroplet device. Previous data have demonstrated this platform's ability to detect both lambda phage DNA and synthetic H3 influenza RNA in a dose-dependent fashion. We now further demonstrate that the platform can effectively differentiate between H1 and H3 strains of influenza, as well as between swine-origin and seasonal strains of H1. Detection using this platform is highly sensitive, amplifying clinical samples as dilute as 102 virions/mL. The tablet platform is described and its efficacy demonstrated elsewhere. Primers were designed using consensus sequences from the online NCBI Influenza Virus Sequence Database to differentiate H3 and H1 swine and seasonal subtypes. SYBR Green I dye was used for real-time fluorescence detection. Each RT-PCR reaction consisted of 50ul of RT-PCR mix from the Superscript III RT-PCR kit. Both reverse transcription and PCR were carried out in the same tube or on the droplet platform. This included 1X Taq buffer, 0.2mM dNTPs, 1.5mM MgCl2 and 0.2uM of both the forward and reverse primers. Cycling consisted of a 30 minute RT step at 50C, followed by 40 cycles of 94C for 15 seconds, the primer-specific annealing temperature for 15 seconds, and 68C for 30 seconds. An initial denature was done following the RT step for 15 minutes for off-chip controls and 2 minutes for platform droplet amplification. We first determined primer efficacy with blinded spiked heat inactivated influenza virus particles in transport media as a serial dilution for the H3N2 and H1N1 (seasonal) subtypes. We effectively identified the sample subtypes which are differentiated by amplicon length. Samples A-H (except C) were positive for the H3 subtype, displayed by the amplicon of 233 bp. Sample C is a negative control, while samples J and K are H1 seasonal positive with an amplicon of 183 bp. The lowest concentration detected was sample E, at 102 virions/ml. We also tested each sample against the other primer sets and found no false positives. We subsequently performed qRT-PCR on the serially diluted samples A-H for the H3 subtype to develop a standard curve for efficiency utilizing our novel tablet platform. We then tested our PCR protocol against clinical samples of unknown subtype and concentration to identify seasonal H1N1, swine H1N1 or seasonal H3N2. The platform is robust, using a simple apparatus and producing results which are both replicable for influenza and applicable to other diseases for which diagnostics currently rely on PCR. Thus, the development of this platform represents an important step towards improved diagnostic technologies for influenza and other infectious diseases.    

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