Gene expression signature-based screening identifies new broadly effective influenza a antivirals.
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.
Centre National de la Recherche Scientifique (CNRS) FRE 3011 Virologie et Pathologie Humaine, Université Lyon 1, Lyon, France. laurence.josset@chu-lyon.fr
Laurence
L
Josset
Julien
J
Textoris
Béatrice
B
Loriod
Olivier
O
Ferraris
Vincent
V
Moules
Bruno
B
Lina
Catherine
C
N'guyen
Jean-Jacques
JJ
Diaz
Manuel
M
Rosa-Calatrava
D000998:Antiviral Agents / Q000494:pharmacology*; D002460:Cell Line; D020869:Gene Expression Profiling; D006801:Humans; D009980:Influenza A virus / Q000187:drug effects* / Q000235:genetics; D008826:Microbial Sensitivity Tests; D020133:Reverse Transcriptase Polymerase Chain Reaction; D014158:Transcription, Genetic
D000998:Antiviral Agents