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  • 1
    Language: English
    In: BMC Bioinformatics, 01 December 2011, Vol.12(1), p.485
    Description: Abstract Background High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. Results We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. Conclusions Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.
    Keywords: Biology
    ISSN: 1471-2105
    E-ISSN: 1471-2105
    Source: Directory of Open Access Journals (DOAJ)
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  • 2
    Language: English
    In: 2013, Vol.9(8), p.e1003561
    Description: Hepatitis C virus (HCV) infection develops into chronicity in 80% of all patients, characterized by persistent low-level replication. To understand how the virus establishes its tightly controlled intracellular RNA replication cycle, we developed the first detailed mathematical model of the initial dynamic phase of the intracellular HCV RNA replication. We therefore quantitatively measured viral RNA and protein translation upon synchronous delivery of viral genomes to host cells, and thoroughly validated the model using additional, independent experiments. Model analysis was used to predict the efficacy of different classes of inhibitors and identified sensitive substeps of replication that could be targeted by current and future therapeutics. A protective replication compartment proved to be essential for sustained RNA replication, balancing translation versus replication and thus effectively limiting RNA amplification. The model predicts that host factors involved in the formation of this compartment determine cellular permissiveness to HCV replication. In gene expression profiling, we identified several key processes potentially determining cellular HCV replication efficiency. ; Hepatitis C is a severe disease and a prime cause for liver transplantation. Up to 3% of the world's population are chronically infected with its causative agent, the Hepatitis C virus (HCV). This capacity to establish long (decades) lasting persistent infection sets HCV apart from other plus-strand RNA viruses typically causing acute, self-limiting infections. A prerequisite for its capacity to persist is HCV's complex and tightly regulated intracellular replication strategy. In this study, we therefore wanted to develop a comprehensive understanding of the molecular processes governing HCV RNA replication in order to pinpoint the most vulnerable substeps in the viral life cycle. For that purpose, we used a combination of biological experiments and mathematical modeling. Using the model to study HCV's replication strategy, we recognized diverse but crucial roles for the membraneous replication compartment of HCV in regulating RNA amplification. We further predict the existence of an essential limiting host factor (or function) required for establishing active RNA replication and thereby determining cellular permissiveness for HCV. Our model also proved valuable to understand and predict the effects of pharmacological inhibitors of HCV and might be a solid basis for the development of similar models for other plus-strand RNA viruses.
    Keywords: Research Article ; Biology ; Medicine
    ISSN: 1553-7366
    E-ISSN: 1553-7374
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  • 3
    Language: English
    In: Cytokine, November 2015, Vol.76(1), pp.105-105
    Description: The pattern recognition receptor RIG-I is a pivotal sensor of viral infections. Its activation by 5′-triphosphorylated- or double-stranded-RNA leads to subsequent signaling via MAVS, TBK1 and IKK epsilon resulting in IRF3 nuclear translocation. Activated IRF3 induces transcription of type I and type III interferons and several interferon stimulated genes. Despite intensive investigations on the RIG-I signaling pathway, its regulatory network still remains largely elusive.To gain more insight into the complex regulation of this pathway a kinome-wide siRNA screen was performed. The primary screen revealed over 100 siRNAs that significantly altered the translocation of IRF3 to the nucleus upon RIG-I stimulation. The top 50 candidates were further analyzed in three independent validation screens based on IRF3-sensitive promoter reporter assays or Rift-valley-fever virus replication. Taking all three validation screens into account, 21 novel regulators of the RIG-I signaling pathway could be identified. Relevance of the identified hits in regulating the host-cell antiviral defense was demonstrated by analyzing cytokine profiles and the impact on Influenza A virus replication.In the course of this screen, DAPK1 was identified as an inhibitor of RIG-I mediated IRF3 activation. Extensive mapping experiments revealed a minimal construct, including the kinase domain, to be sufficient for inhibiting IRF3 reporter activation in over-expression experiments. Furthermore, interaction studies revealed binding of DAPK1 to ligand-activated RIG-I, suggesting that a DAPK1 mediated phosphorylation of RIG-I inhibits its activity. In fact, in an in vitro kinase assays we could demonstrate that RIG-I is a substrate of DAPK1.
    Keywords: Medicine ; Biology
    ISSN: 1043-4666
    E-ISSN: 1096-0023
    Source: ScienceDirect Journals (Elsevier)
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  • 4
    Language: English
    In: BMC bioinformatics, 20 December 2011, Vol.12, pp.485
    Description: High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.
    Keywords: RNA Interference ; Dengue -- Genetics ; Hepatitis C -- Genetics ; Phosphotransferases -- Genetics ; Single-Cell Analysis -- Methods
    E-ISSN: 1471-2105
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  • 5
    Language: English
    In: Molecular Cell, 02 February 2017, Vol.65(3), pp.403-415.e8
    Description: Cell-autonomous induction of type I interferon must be stringently regulated. Rapid induction is key to control virus infection, whereas proper limitation of signaling is essential to prevent immunopathology and autoimmune disease. Using unbiased kinome-wide RNAi screening followed by thorough validation, we identified 22 factors that regulate RIG-I/IRF3 signaling activity. We describe a negative-feedback mechanism targeting RIG-I activity, which is mediated by death associated protein kinase 1 (DAPK1). RIG-I signaling triggers DAPK1 kinase activation, and active DAPK1 potently inhibits RIG-I stimulated IRF3 activity and interferon-beta production. DAPK1 phosphorylates RIG-I in vitro at previously reported as well as other sites that limit 5′ppp-dsRNA sensing and virtually abrogate RIG-I activation. Willemsen et al. screened the antiviral RIG-I pathway for regulators and identified and validated 22 kinases. They describe an inhibitory feedback loop mediated by DAPK1. Antiviral signaling activates DAPK1 kinase activity, which, in turn, inactivates RIG-I by direct phosphorylation.
    Keywords: Innate Immunity ; Antiviral Response ; Pattern Recognition Receptors ; Signal Transduction ; Feedback Regulation ; Interferon System ; Cytokines ; Dapk1 ; Rig-I ; Ddx58 ; Biology
    ISSN: 1097-2765
    E-ISSN: 1097-4164
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