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RESEARCH PROFILES MChip: the next generation of influenza surveillance Fluor-tagged label
Target RNA Capture
KATHY ROWLEN
The threat of a worldwide flu pandemic has public-health experts scrambling to classify and track circulating and emerging influenza viruses. However, the standard method for classifying influenza involves a tedious, 2-week procedure of cultivating the virus in chicken eggs or tissue culture followed by an antibody-based assay. Newer technologies, such as high-density microarrays and gene sequencing, are faster but expensive. In the November 15 issue of Analytical Chemistry (pp 7610–7615), Erica Dawson, Kathy Rowlen, and colleagues at the University of Colorado at Boulder, InDevR, and the Centers for Disease Control and Prevention (CDC) introduce a rapid, low-cost method for subtyping influenza virus with low-density microarrays. Influenza viruses are typed into three categories (A, B, and C) according to differences in viral matrix (M) proteins and nucleoproteins. Influenza A, responsible for most flu-related deaths, is further subtyped according to the antigenic characteristics of the hemagglutinin (HA) and neuraminidase (NA) surface glycoproteins. There are 16 HA subtypes (H1–H16) and 9 NA subtypes (N1–N9), for a total of 144 possible combinations, although not all subtype combinations replicate efficiently. Every year, CDC and the World Health Organization collect samples from around the world to determine the most prevalent circulating strains, which are then included in the next flu vaccine. Influenza microarrays for viral subtyping are commercially available, but because they assay thousands of different viral sequences, they are expensive. Rowlen says, “To enhance global influenza surveillance, we need to obtain more samples from largely rural locations, like Cambodia and Indonesia, where the amount of money spent on health care per person is only a few dollars per year. Our idea was to use the high information content of microarrays but to make them globally affordable.”
Amplified influenza RNA binds to immobilized capture oligonucleotides specific for viral type or subtype. A label oligonucleotide also hybridizes to the RNA and generates a fluorescent signal. (Bottom) Fluorescence pattern for influenza A H1N1.
Rowlen and colleagues developed a low-density influenza microarray that provides complete subtype information at a fraction of the cost of high-density microarrays. Previous influenza microarrays examined sequences from multiple genes (minimally, M, HA, and NA genes) to determine type and subtype; this approach requires many expensive fluorescently labeled probes. Dawson noticed that microarray hybridization patterns for the M gene were distinct for different influenza subtypes. Previously, the M gene had been used to type but not to subtype the virus, because the gene sequence was thought to be too conserved for subtyping. According to Dawson, “Although the M gene segment mutates less rapidly than the HA or NA gene, it clearly carries some sort of genetic signature that allows us to distinguish among subtypes.”
After making this discovery, Dawson, Rowlen, and co-workers designed an influenza microarray composed only of M gene sequences. Eliminating HA and NA sequences simplified reverse transcriptase PCR amplification of viral genomic RNA because multiple primer sets and reaction conditions were no longer necessary. A computer algorithm helped the researchers to identify conserved regions from M gene segments of many different influenza A subtypes. Fifteen sequences were chosen because they were predicted to be either broadly reactive or specific for a certain subtype. These 15 M gene sequences were immobilized on a glass slide to form a low-density influenza microarray, which the researchers call the MChip. They characterized the chip by using influenza A isolates representing a variety of subtypes. Hybridization of viral RNA to microarray sequences and fluorescence detection revealed that all viruses of the same subtype had a similar and distinct fluorescence pattern. To automate image interpretation, they developed an artificial neural network (ANN). The ANN was trained to recognize the distinct MChip fluorescence pattern for each subtype as well as for influenza-A-negative samples (e.g., influenza B). The ANN was then put to the test with MChip data from patient samples in a blind study. It correctly identified 50 of the 53 samples as a particular subtype or as negative for influenza A. The MChip is more rapid than other methods that provide complete subtype information, requiring ~7 h from patient sample to fluorescence image. Rowlen envisions that integrating all reactions in a single instrument might further reduce assay time. She says, “If you were going to use the MChip in the field in rural locations, you’d ideally have a handheld device where you’d put in a sample and the device would report the answer a short time later—sort of like the Star Trek tricorder.” a —Laura Tomky Cassiday
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