No abstract

Influenza A viruses, which are further subtyped on the basis of antigenic differences in external hemagglutinin and neuraminidase glycoproteins, and influenza B viruses are prominent among the viral causes of respiratory diseases and can cause a wide spectrum of illness. Each year these viruses are responsible for recurrent epidemics, frequently in association with genetic variation. There is a requirement for sensitive and rapid diagnostic techniques in order to improve both the diagnosis of infections and the quality of surveillance systems. A new three-dimensional biochip platform (Flow-Thru Chip; MetriGenix) was used to develop a rapid and reliable molecular method for the typing and subtyping of influenza viruses. Oligonucleotide probes immobilized in microchannels of a silicon wafer were selected to recognize multiple fragments of the influenza A virus matrix protein gene; the influenza B virus NS gene; the H1, H3, and H5 hemagglutinin genes; and the N1 and N2 neuraminidase genes. Biotinylated amplicons resulting from either multiplex or random reverse transcription-PCR were hybridized to arrayed oligonucleotides on the influenza virus chip before they were stained with horseradish peroxidase-streptavidin and were imaged by use of a chemiluminescent substrate. The chip analysis procedure, from the time of pipetting of the sample into the chip cartridge to the time of analysis of the results, was performed in less than 5 h. The random PCR exhibited a higher level of performance than the multiplex PCR in terms of the specificity of product hybridization to the influenza virus chip. Analysis of influenza A viruses (H1N1, H3N2, H1N2, and H5N1) and influenza B viruses showed that this microarraybased method is capable of the rapid and unambiguous identification of all types and subtypes of viruses by use of random PCR products. The redundancy of the probes designed for each gene selected yielded an additional criterion of confidence for the subtyping of viruses which are known for antigenic variations in some of their components.

Classical antiviral therapies target viral proteins and are consequently subject to resistance. To counteract this limitation, alternative strategies have been developed that target cellular factors. We hypothesized that such an approach could also be useful to identify broad-spectrum antivirals. The influenza A virus was used as a model for its viral diversity and because of the need to develop therapies against unpredictable viruses as recently underlined by the H1N1 pandemic. We proposed to identify a gene-expression signature associated with infection by different influenza A virus subtypes which would allow the identification of potential antiviral drugs with a broad anti-influenza spectrum of activity. We analyzed the cellular gene expression response to infection with five different human and avian influenza A virus strains and identified 300 genes as differentially expressed between infected and non-infected samples. The most 20 dysregulated genes were used to screen the connectivity map, a database of drug-associated gene expression profiles. Candidate antivirals were then identified by their inverse correlation to the query signature. We hypothesized that such molecules would induce an unfavorable cellular environment for influenza virus replication. Eight potential antivirals including ribavirin were identified and their effects were tested in vitro on five influenza A strains. Six of the molecules inhibited influenza viral growth. The new pandemic H1N1 virus, which was not used to define the gene expression signature of infection, was inhibited by five out of the eight identified molecules, demonstrating that this strategy could contribute to identifying new broad anti-influenza agents acting on cellular gene expression. The identified infection signature genes, the expression of which are modified upon infection, could encode cellular proteins involved in the viral life cycle. This is the first study showing that gene expression-based screening can be used to identify antivirals. Such an approach could accelerate drug discovery and be extended to other pathogens.