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  • Knapp, Bettina  (16)
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  • 1
    In: Bioinformatics, 2015, Vol. 31(19), pp.3231-3233
    Description: Summary: With the widespread availability of high-throughput experimental technologies it has become possible to study hundreds to thousands of cellular factors simultaneously, such as coding- or non-coding mRNA or protein concentrations. Still, extracting information about the underlying regulatory or signaling interactions from these data remains a difficult challenge. We present a flexible approach towards network inference based on linear programming. Our method reconstructs the interactions of factors from a combination of perturbation/non-perturbation and steady-state/time-series data. We show both on simulated and real data that our methods are able to reconstruct the underlying networks fast and efficiently, thus shedding new light on biological processes and, in particular, into disease’s mechanisms of action. We have implemented the approach as an R package available through bioconductor. Availability and implementation: This R package is freely available under the Gnu Public License (GPL-3) from bioconductor.org ( http://bioconductor.org/packages/release/bioc/html/lpNet.html ) and is compatible with most operating systems (Windows, Linux, Mac OS) and hardware architectures. Contact: bettina.knapp@helmholtz-muenchen.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Keywords: Biology;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
    E-ISSN: 13674811
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  • 2
    In: PLoS ONE, 2013, Vol.8(7)
    Description: Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4 + T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.
    Keywords: Research Article ; Biology ; Computer Science
    E-ISSN: 1932-6203
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  • 3
    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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  • 4
    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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  • 5
    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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  • 6
    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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  • 7
    Language: English
    In: Molecular Therapy, December 2014, Vol.22(12), pp.2130-2141
    Description: Malaria, caused by protozoan parasites, remains a prevalent infectious human disease due to the lack of an efficient and safe vaccine. This is directly related to the persisting gaps in our understanding of the parasite's interactions with the infected host, especially during the clinically silent yet essential liver stage of development. Previously, we and others showed that genetically attenuated parasites (GAP) that arrest in the liver induce sterile immunity, but only upon multiple administrations. Here, we comprehensively studied hepatic gene and miRNA expression in GAP-injected mice, and found both a broad activation of IFNγ-associated pathways and a significant increase of murine microRNA-155 (miR-155), that was especially pronounced in non-parenchymal cells including liver-resident macrophages (Kupffer cells). Remarkably, ectopic upregulation of this miRNA in the liver of mice using robust hepatotropic adeno-associated virus 8 (AAV8) vectors enhanced GAP's protective capacity substantially. In turn, this AAV8-mediated miR-155 expression permitted a reduction of GAP injections needed to achieve complete protection against infectious parasite challenge from previously three to only one. Our study highlights a crucial role of mammalian miRNAs in liver infection and concurrently implies their great potential as future immune-augmenting agents in improved vaccination regimes against malaria and other diseases.
    Keywords: Medicine ; Biology
    ISSN: 1525-0016
    E-ISSN: 1525-0024
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  • 8
    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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  • 9
    Language: English
    In: Cytokine, 11/2015, Vol.76(1), p.105
    ISSN: 10434666
    Source: Elsevier (via CrossRef)
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  • 10
    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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