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A gene signature-based approach identifies mTOR as a regulator of p73.

ABSTRACT: Although genomic technologies have advanced the characterization of gene regulatory networks downstream of transcription factors, the identification of pathways upstream of these transcription factors has been more challenging. In this study we present a gene signature-based approach for connecting signaling pathways to transcription factors, as exemplified by p73. We generated a p73 gene signature by integrating whole-genome chromatin immunoprecipitation and expression profiling. The p73 signature was linked to corresponding signatures produced by drug candidates, using the in silico Connectivity Map resource, to identify drugs that would induce p73 activity. Of the pharmaceutical agents identified, there was enrichment for direct or indirect inhibitors of mammalian Target of Rapamycin (mTOR) signaling. Treatment of both primary cells and cancer cell lines with rapamycin, metformin, and pyrvinium resulted in an increase in p73 levels, as did RNA interference-mediated knockdown of mTOR. Further, a subset of genes associated with insulin response or autophagy exhibited mTOR-mediated, p73-dependent expression. Thus, downstream gene signatures can be used to identify upstream regulators of transcription factor activity, and in doing so, we identified a new link between mTOR, p73, and p73-regulated genes associated with autophagy and metabolic pathways.

SUBMITTER: Rosenbluth JM

PROVIDER: S-EPMC2547001 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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A gene signature-based approach identifies mTOR as a regulator of p73.

Rosenbluth Jennifer M JM Mays Deborah J DJ Pino Maria F MF Tang Luo Jia LJ Pietenpol Jennifer A JA

Molecular and cellular biology 20080804 19

Although genomic technologies have advanced the characterization of gene regulatory networks downstream of transcription factors, the identification of pathways upstream of these transcription factors has been more challenging. In this study we present a gene signature-based approach for connecting signaling pathways to transcription factors, as exemplified by p73. We generated a p73 gene signature by integrating whole-genome chromatin immunoprecipitation and expression profiling. The p73 signat ...[more]

PMID: 18678646

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Project description: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.

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.

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A gene signature-based approach identifies mTOR as a regulator of p73.

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A gene signature-based approach identifies mTOR as a regulator of p73.

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