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Gene expression signature-based screening identifies new broadly effective influenza a antivirals.

ABSTRACT: 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.

SUBMITTER: Josset L

PROVIDER: S-EPMC2949399 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Gene expression signature-based screening identifies new broadly effective influenza a antivirals.

Josset Laurence L Textoris Julien J Loriod Béatrice B Ferraris Olivier O Moules Vincent V Lina Bruno B N'guyen Catherine C Diaz Jean-Jacques JJ Rosa-Calatrava Manuel M

PloS one 20101004 10

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 i ...[more]

PMID: 20957181

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Project description:Classical antiviral therapy inhibit viral proteins and are subject to resistance. To counteract this emergence, alternative strategy has been developed that target cellular factors. We hypothesized that such approach could also be useful to identify broad antivirals. Influenza A virus was used as a model for viral diversity and need for therapy against unpredictable viruses as recently underlined by the H1N1 pandemic. We proposed to identify a gene-expression signature associated with infection with different influenza A virus subtypes which could help to identify potential antiviral drugs with broad spectrum. Cellular gene expression response to infection with five different human and avian influenza viruses strains was analyzed and 300 genes were determined as differentially expressed between infected and non-infected samples. Strikingly, only a few genes were induced by infection and related to immune response. A more concise list was used to screen connectivity map, a database of drug-associated gene expression profiles, for molecules with inverse profiles than the signature of infection. We hypothesized that such compounds would induce an unfavorable cellular environment for influenza virus replication. Eight potential antivirals including ribavirin were identified, and six inhibited influenza viral growth in vitro. The new pandemic H1N1 virus, which was not used to define the gene expression signature of infection, was inhibited by five of the eight identified molecules, demonstrating that this strategy could help to identify broad spectrum antivirals. This is the first study showing that a gene expression based-screening can be used to identify antivirals. Such approaches could accelerate the drug discovery progress and could be extended to other pathogens. A549 (human lung epithelial cells) were infected with 5 different influenza A strains (A/New Caledonia/20/99 (H1N1), A/Moscow/10/99 (H3N2), A/Lyon/969/09 (H1N1 SOI-V), A/Turkey/582/2006 (H5N1), A/Finch/England/2051/94 (H5N2), and A/Chicken/Italy/2076/99 (H7N1)) or mock infected. Five independant replicates were done and hybridized on a different microarray. The overall design is thus composed of 5 mock samples, and 5x5 infected samples.

Project description:Background & aimsChronic hepatitis B is an incurable disease. Addressing the unmet medical need for therapies has been hampered by a lack of suitable cell culture models to investigate the HBV life cycle in a single experimental setup. We sought to develop a platform suitable to investigate all aspects of the entire HBV life cycle.MethodsHepG2-NTCPsec+ cells were inoculated with HBV. Supernatants of infected cells were transferred to naïve cells. Inhibition of infection was determined in primary and secondary infected cells by high-content imaging of viral and cellular factors. Novel antivirals were triaged in cells infected with cell culture- or patient-derived HBV and in stably virus replicating cells. HBV internalisation and target-based receptor binding assays were conducted.ResultsWe developed an HBV platform, screened 2,102 drugs and bioactives, and identified 3 early and 38 late novel HBV life cycle inhibitors using infectious HBV genotype D. Two early inhibitors, pranlukast (EC50 4.3 μM; 50% cytotoxic concentration [CC50] >50 μM) and cytochalasin D (EC50 0.07 μM; CC50 >50 μM), and 2 late inhibitors, fludarabine (EC50 0.1 μM; CC50 13.4 μM) and dexmedetomidine (EC50 6.2 μM; CC50 >50 μM), were further investigated. Pranlukast inhibited HBV preS1 binding, whereas cytochalasin D prevented the internalisation of HBV. Fludarabine inhibited the secretion of HBV progeny DNA, whereas dexmedetomidine interfered with the infectivity of HBV progeny. Patient-derived HBV genotype C was efficiently inhibited by fludarabine (EC50 0.08 μM) and dexmedetomidine (EC50 8.7 μM).ConclusionsThe newly developed high-content assay is suitable to screen large-scale drug libraries, enables monitoring of the entire HBV life cycle, and discriminates between inhibition of early and late viral life cycle events.Lay summaryHBV infection is an incurable, chronic disease with few available treatments. Addressing this unmet medical need has been hampered by a lack of suitable cell culture models to study the entire viral life cycle in a single experimental setup. We developed an image-based approach suitable to screen large numbers of drugs, using a cell line that can be infected by HBV and produces large amounts of virus particles. By transferring viral supernatants from these infected cells to uninfected target cells, we could monitor the entire viral life cycle. We used this system to screen drug libraries and identified novel anti-HBV inhibitors that potently inhibit HBV in various phases of its life cycle. This assay will be an important new tool to study the HBV life cycle and accelerate the development of novel therapeutic strategies.

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Gene expression signature-based screening identifies new broadly effective influenza a antivirals.

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Gene expression signature-based screening identifies new broadly effective influenza a antivirals.

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