Martin Mehlmann

Experimental Evaluation of the FluChip Diagnostic Microarray for Influenza Virus Surveillance

ABSTRACT Global surveillance of influenza is critical for improvements in disease management and is especially important for early detection, rapid intervention, and a possible reduction of the impact of an influenza pandemic. Enhanced surveillance requires rapid, robust, and inexpensive analytical techniques capable of providing a detailed analysis of influenza virus strains. Low-density oligonucleotide microarrays with highly multiplexed “signatures” for influenza viruses offer many of the desired characteristics. However, the high mutability of the influenza virus represents a design challenge. In order for an influenza virus microarray to be of utility, it must provide information for a wide range of viral strains and lineages. The design and characterization of an influenza microarray, the FluChip-55 microarray, for the relatively rapid identification of influenza A virus subtypes H1N1, H3N2, and H5N1 are described here. In this work, a small set of sequences was carefully selected to exhibit broad coverage for the influenza A and B viruses currently circulating in the human population as well as the avian A/H5N1 virus that has become enzootic in poultry in Southeast Asia and that has recently spread to Europe. A complete assay involving extraction and amplification of the viral RNA was developed and tested. In a blind study of 72 influenza virus isolates, RNA from a wide range of influenza A and B viruses was amplified, hybridized, labeled with a fluorophore, and imaged. The entire analysis time was less than 12 h. The combined results for two assays provided the absolutely correct types and subtypes for an average of 72% of the isolates, the correct type and partially correct subtype information for 13% of the isolates, the correct type only for 10% of the isolates, false-negative signals for 4% of the isolates, and false-positive signals for 1% of the isolates. In the overwhelming majority of cases in which incomplete subtyping was observed, the failure was due to the nucleic acid amplification step rather than limitations in the microarray.

Comparison of the MChip to Viral Culture, Reverse Transcription-PCR, and the QuickVue Influenza A+B Test for Rapid Diagnosis of Influenza

ABSTRACT The performance of a diagnostic microarray (the MChip assay) for influenza was compared in a blind study to that of viral culture, reverse transcription (RT)-PCR, and the QuickVue Influenza A+B test. The patient sample data set was composed of 102 respiratory secretion specimens collected between 29 December 2005 and 2 February 2006 at Scott & White Hospital and Clinic in Temple, Texas. Samples were collected from a wide range of age groups by using direct collection, nasal/nasopharyngeal swabs, or nasopharyngeal aspiration. Viral culture and the QuickVue assay were performed at the Texas site at the time of collection. Aliquots for each sample, identified only by study numbers, were provided to the University of Colorado and Vanderbilt University teams for blinded analysis. When referenced to viral culture, the MChip exhibited a clinical sensitivity of 98% and a clinical specificity of 98%. When referenced to RT-PCR, the MChip assay exhibited a clinical sensitivity of 92% and a clinical specificity of 98%. While the MChip assay currently requires 7 to 8 h to complete the analysis, a significant advantage of the test for influenza virus-positive samples is simultaneous detection and full subtype identification for the two subtypes currently circulating in humans (A/H3N2 and A/H1N1) and avian (A/H5N1) viruses.

Robust Sequence Selection Method Used To Develop the FluChip Diagnostic Microarray for Influenza Virus

ABSTRACT DNA microarrays have proven to be powerful tools for gene expression analyses and are becoming increasingly attractive for diagnostic applications, e.g., for virus identification and subtyping. The selection of appropriate sequences for use on a microarray poses a challenge, particularly for highly mutable organisms such as influenza viruses, human immunodeficiency viruses, and hepatitis C viruses. The goal of this work was to develop an efficient method for mining large databases in order to identify regions of conservation in the influenza virus genome. From these regions of conservation, capture and label sequences capable of discriminating between different viral types and subtypes were selected. The salient features of the method were the use of phylogenetic trees for data reduction and the selection of a relatively small number of capture and label sequences capable of identifying a broad spectrum of influenza viruses. A detailed experimental evaluation of the selected sequences is described in a companion paper. The software is freely available under the General Public License at http://www.colorado.edu/chemistry/RGHP/software/ .