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  • Rohr, Karl  (13)
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  • 1
    Language: English
    In: BMC Bioinformatics, Dec 20, 2011, Vol.12, p.485
    Description: 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: Genes -- Identification And Classification ; Genetic Testing -- Methods ; Genetic Testing -- Research ; Rna Interference -- Physiological Aspects ; Rna Interference -- Usage
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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  • 2
    Language: English
    In: PLoS ONE, 01 January 2012, Vol.7(12), p.e52555
    Description: miRNA cluster miR-17-92 is known as oncomir-1 due to its potent oncogenic function. miR-17-92 is a polycistronic cluster that encodes 6 miRNAs, and can both facilitate and inhibit cell proliferation. Known targets of miRNAs encoded by this cluster are largely regulators of cell cycle progression and apoptosis. Here, we show that miRNAs encoded by this cluster and sharing the seed sequence of miR-17 exert their influence on one of the most essential cellular processes - endocytic trafficking. By mRNA expression analysis we identified that regulation of endocytic trafficking by miR-17 can potentially be achieved by targeting of a number of trafficking regulators. We have thoroughly validated TBC1D2/Armus, a GAP of Rab7 GTPase, as a novel target of miR-17. Our study reveals regulation of endocytic trafficking as a novel function of miR-17, which might act cooperatively with other functions of miR-17 and related miRNAs in health and disease.
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
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  • 3
    Language: English
    In: BMC Bioinformatics, Dec 20, 2011, Vol.12, p.485
    Description: 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: Genes -- Identification And Classification ; Genetic Testing -- Methods ; Genetic Testing -- Research ; Rna Interference -- Physiological Aspects ; Rna Interference -- Usage
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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  • 4
    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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  • 5
    Language: English
    In: 2015, Vol.11(12), p.e1005281
    Description: Adeno-associated viruses are members of the genus dependoviruses of the parvoviridae family. AAV vectors are considered promising vectors for gene therapy and genetic vaccination as they can be easily produced, are highly stable and non-pathogenic. Nevertheless, transduction of cells in vitro and in vivo by AAV in the absence of a helper virus is comparatively inefficient requiring high multiplicity of infection. Several bottlenecks for AAV transduction have previously been described, including release from endosomes, nuclear transport and conversion of the single stranded DNA into a double stranded molecule. We hypothesized that the bottlenecks in AAV transduction are, in part, due to the presence of host cell restriction factors acting directly or indirectly on the AAV-mediated gene transduction. In order to identify such factors we performed a whole genome siRNA screen which identified a number of putative genes interfering with AAV gene transduction. A number of factors, yielding the highest scores, were identified as members of the SUMOylation pathway. We identified Ubc9, the E2 conjugating enzyme as well as Sae1 and Sae2, enzymes responsible for activating E1, as factors involved in restricting AAV. The restriction effect, mediated by these factors, was validated and reproduced independently. Our data indicate that SUMOylation targets entry of AAV capsids and not downstream processes of uncoating, including DNA single strand conversion or DNA damage signaling. We suggest that transiently targeting SUMOylation will enhance application of AAV in vitro and in vivo . ; SUMOylation is a post-translational modification in which a small protein (SUMO) is covalently attached to target proteins. Three key enzymes are controlling this modification: The E1 activating complex composed of the heterodimer Sae1/Sae2, the E2 conjugation enzyme Ubc9 and one of many E3 enzymes which specifically recognize the target protein. SUMOylation regulates many processes such as protein stability, intracellular localization and protein-protein interactions. In our study we identified SUMOylation to be regulating transduction of cells by the human parvovirus adeno-associated virus (AAV). Targeting the E1 or E2 complex by RNA interference led to increased AAV transduction. We also identified putative E3 enzymes involved in this mechanism. Our data indicates that this regulation is mediated by the AAV capsid and it affects different AAV serotypes. Targeting SUMOylation might be a strategy to enhance AAV gene transduction.
    Keywords: Research Article
    ISSN: 1553-7366
    E-ISSN: 1553-7374
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  • 6
    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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  • 7
    Language: English
    In: Cytokine, 11/2015, Vol.76(1), p.105
    ISSN: 10434666
    Source: Elsevier (via CrossRef)
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  • 8
    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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  • 9
    Language: English
    In: Bioinformatics (Oxford, England), 15 September 2010, Vol.26(18), pp.i653-8
    Description: Detecting human proteins that are involved in virus entry and replication is facilitated by modern high-throughput RNAi screening technology. However, hit lists from different laboratories have shown only little consistency. This may be caused by not only experimental discrepancies, but also not fully explored possibilities of the data analysis. We wanted to improve reliability of such screens by combining a population analysis of infected cells with an established dye intensity readout. Viral infection is mainly spread by cell-cell contacts and clustering of infected cells can be observed during spreading of the infection in situ and in vivo. We employed this clustering feature to define knockdowns which harm viral infection efficiency of human Hepatitis C Virus. Images of knocked down cells for 719 human kinase genes were analyzed with an established point pattern analysis method (Ripley's K-function) to detect knockdowns in which virally infected cells did not show any clustering and therefore were hindered to spread their infection to their neighboring cells. The results were compared with a statistical analysis using a common intensity readout of the GFP-expressing viruses and a luciferase-based secondary screen yielding five promising host factors which may suit as potential targets for drug therapy. We report of an alternative method for high-throughput imaging methods to detect host factors being relevant for the infection efficiency of viruses. The method is generic and has the potential to be used for a large variety of different viruses and treatments being screened by imaging techniques.
    Keywords: Image Processing, Computer-Assisted ; RNA Interference ; RNA, Small Interfering ; Virus Replication ; Biological Factors -- Analysis ; Hepacivirus -- Physiology
    ISSN: 13674803
    E-ISSN: 1367-4811
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  • 10
    Language: English
    In: Cell Host & Microbe, 19 July 2012, Vol.12(1), pp.71-85
    Description: Virus infection-induced global protein synthesis suppression is linked to assembly of stress granules (SGs), cytosolic aggregates of stalled translation preinitiation complexes. To study long-term stress responses, we developed an imaging approach for extended observation and analysis of SG dynamics during persistent hepatitis C virus (HCV) infection. In combination with type 1 interferon, HCV infection induces highly dynamic assembly/disassembly of cytoplasmic SGs, concomitant with phases of active and stalled translation, delayed cell division, and prolonged cell survival. Double-stranded RNA (dsRNA), independent of viral replication, is sufficient to trigger these oscillations. Translation initiation factor eIF2α phosphorylation by protein kinase R mediates SG formation and translation arrest. This is antagonized by the upregulation of GADD34, the regulatory subunit of protein phosphatase 1 dephosphorylating eIF2α. Stress response oscillation is a general mechanism to prevent long-lasting translation repression and a conserved host cell reaction to multiple RNA viruses, which HCV may exploit to establish persistence. ► Persistent HCV infection combined with IFN induces highly dynamic stress response ► Stress granule (SG) assembly/disassembly prevents prolonged translation repression ► Control of SG oscillations by opposing kinase PKR and protein phosphatase 1 actions ► SG oscillation is a conserved host cell response to multiple RNA virus infections
    Keywords: Biology
    ISSN: 1931-3128
    E-ISSN: 1934-6069
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