QUICK DIAGNOSIS OF FLU STRAINS POSSIBLE WITH NEW MICROCHIP TEST
Scientists from the University of Colorado at Boulder and the Centers for Disease Control and Prevention (CDC) have developed a microchip-based test that may allow more labs to diagnose influenza infections and learn more about the viruses causing illness. The FluChip successfully distinguished among 72 influenza strains — including the H5N1 avian influenza strain — in less than 12 hours. The research was led by University of Colorado scientist Kathy L. Rowlen, Ph.D., and was funded by the National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health (NIH). It appears in the current issue of the Journal of Clinical Microbiology.
Laboratories across the United States can do basic tests to determine the type and subtype of an influenza virus within several hours. However, only the CDC and a handful of other labs internationally have the high-level biosafety facilities needed to perform specialized tests that reveal critical details about the virus’s geographic origin and other features. Because the FluChip technology could be used in lower level biosafety facilities, it could expand influenza diagnostic capacity by allowing more labs to determine the geographic origin of a newly emergent virus and whether its source is human or nonhuman; learn how closely related a new virus is to ones that circulated previously; and detect genetic changes that may signal the virus is becoming more virulent.
“The ability to quickly and accurately identify strains of influenza would be invaluable to international flu surveillance efforts,” says NIAID Director Anthony S. Fauci, M.D. “This is an encouraging advance.”
“This state-of-the-art research is vital to our efforts to protect the nation’s health, and it may provide a new tool in our toolbox in the fight against influenza,” says CDC Director Julie Gerberding, M.D. “This is an excellent example of the advances we can achieve when governmental and academic researchers work together, and we look forward to future collaboration.”
The FluChip is a type of microarray, commonly called a gene chip. Although there are numerous variations, microarrays can be made by using a robotic arm to drop hundreds or thousands of spots of genetic material — DNA or RNA — of known sequence onto a microscope slide. The spots, called probes, are then exposed to a sample of unknown composition: for instance, material taken from a person with an undiagnosed illness. Probes that match gene sequences of bacteria or viruses present in the sample result in capture of the target gene. By analyzing the pattern of captured targets, doctors can diagnose the cause of infection.
A key challenge in designing a gene chip for flu diagnosis is determining which flu virus gene sequences to use as probes, notes Dr. Rowlen. In a companion paper, the researchers describe a powerful new way to scan vast amounts of flu virus genetic information to find the most informative sequences. “Our goal was to develop an efficient method for mining large databases to identify regions of the flu genome that are largely the same from strain to strain as well as strain-specific sequences,” Dr. Rowlen says.
Beginning with a pool of nearly 5,000 flu gene sequences, the investigators used the data mining process to select 55 flu RNA sequences for use as probes on the FluChip. Among them were probes chosen to enable detection of two of the most common flu strains currently circulating in humans, the H1N1 and H3N2 strains, as well as the avian flu strain H5N1.
The CDC provided flu isolates to the University of Colorado researchers to identify using the FluChip. The samples included flu strains that infect humans, horses, birds and swine. CDC shared its technical expertise on influenza and worked alongside University of Colorado staff in CDC laboratories to process the influenza samples, test the FluChip technology and analyze the results. Combined results after two rounds of tests showed that the FluChip allowed users to obtain correct information about both type and subtype — considered a full characterization of a strain — from 72 percent of the samples. Full information on type — but only partial information on subtype — was obtained for 13 percent of the samples, while 10 percent of the samples could be identified by type only (no information about subtype). It took about 11 hours to conduct the tests and learn the identities of the strains, report the scientists.
“We were surprised and pleased at how well the chip performed in these early tests,” says Dr. Rowlen. The researchers from CDC and University of Colorado are continuing to refine the FluChip and hope to bring the total time required to get full type and subtype characterization to under one hour.
Developing improved gene chips for flu diagnosis depends, in part, on the ready public availability of genomic sequence data, notes Karen Lacourciere, Ph.D., NIAID influenza program officer. In addition to flu genome sequence databases housed at the Los Alamos National Laboratory and CDC, the researchers also used information from the NIAID-supported Influenza Genome Sequencing Project. NIAID rapidly makes this sequence information publicly available through GenBank, an international, searchable online database funded by NIH. This resource is enhancing researchers’ ability to select target sequences for next generation diagnostic chips, Dr. Lacourciere says.
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M Mehlmann et al. Robust sequence selection method used to develop the FluChip diagnostic microarray for influenza virus. Journal of Clinical Microbiology. DOI: 10.1128/JCM.00135-06 (Aug. 2006)
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