Abstract
The emergence and rapid spread of the 2009 H1N1 pandemic influenza virus showed that many diagnostic tests were unsuitable for detecting the novel virus isolates. In most countries the probe-based TaqMan assay developed by the U.S. Centers for Disease Control and Prevention was used for diagnostic purposes. The substantial sequence data that became available during the course of the pandemic created the opportunity to utilize bioinformatics tools to evaluate the unique sequence properties of this virus for the development of diagnostic tests. We used a comprehensive computational approach to examine conserved 2009 H1N1 sequence signatures that are at least 20 nucleotides long and contain at least two mismatches compared to any other known H1N1 genome. We found that the hemagglutinin (HA) and neuraminidase (NA) genes contained sequence signatures that are highly conserved among 2009 H1N1 isolates. Based on the NA gene signatures, we used Visual-OMP to design primers with optimal hybridization affinity and we used ThermoBLAST to minimize amplification artifacts. This procedure resulted in a highly sensitive and discriminatory 2009 H1N1 detection assay. Importantly, we found that the primer set can be used reliably in both a conventional TaqMan and a SYBR green reverse transcriptase (RT)-PCR assay with no loss of specificity or sensitivity. We validated the diagnostic accuracy of the NA SYBR green assay with 125 clinical specimens obtained between May and August 2009 in Chile, and we showed diagnostic efficacy comparable to the CDC assay. Our approach highlights the use of systematic computational approaches to develop robust diagnostic tests during a viral pandemic.