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  • Froehlich, Holger  (15)
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  • 1
    Language: English
    In: BMC Bioinformatics, Oct 15, 2007, Vol.8(386), p.386
    Description: Background The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. Results In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. Conclusion Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-[alpha] pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package nem, which can be obtained from the Bioconductor homepage.
    Keywords: Cellular Signal Transduction -- Analysis ; Rna Interference -- Usage ; Dna Microarrays -- Usage ; Gene Expression -- Analysis
    ISSN: 1471-2105
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  • 2
    Language: English
    In: PLOS ONE, 2013, Vol.8(10), pp.urn:issn:1932-6203
    Description: Transforming growth factor-beta 1 (TGF-[beta]1) stimulates a broad range of effects which are cell type dependent, and it has been suggested to induce cellular senescence. On the other hand, long-term culture of multipotent mesenchymal stromal cells (MSCs) has a major impact on their cellular physiology and therefore it is well conceivable that the molecular events triggered by TGF-[beta]1 differ considerably in cells of early and late passages. In this study, we analyzed the effect of TGF-[beta]1 on and during replicative senescence of MSCs. Stimulation with TGF-[beta]1 enhanced proliferation, induced a network like growth pattern and impaired adipogenic and osteogenic differentiation. TGF-[beta]1 did not induce premature senescence. However, due to increased proliferation rates the cells reached replicative senescence earlier than untreated controls. This was also evident, when we analyzed senescence-associated DNA-methylation changes. Gene expression profiles of MSCs differed considerably at relatively early (P 3 - 5) and later passages (P 10). Nonetheless, relative gene expression differences provoked by TGF-[beta]1 at individual time points or in a time course dependent manner (stimulation for 0, 1, 4 and 12 h) were very similar in MSCs of early and late passage. These results support the notion that TGF-[beta]1 has major impact on MSC function, but it does not induce senescence and has similar molecular effects during culture expansion.
    Keywords: Methylation -- Analysis ; Bone Morphogenetic Proteins -- Analysis ; Stem Cells -- Analysis ; Genes -- Analysis ; Transforming Growth Factors -- Analysis ; Gene Expression -- Analysis;
    ISSN: 1932-6203
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  • 3
    Language: English
    Description: Background: Gene perturbation experiments in combination with fluorescence time-lapse cell imaging are a powerful tool in reverse genetics. High content applications require tools for the automated processing of the large amounts of data. These tools include in general several image processing steps, the extraction of morphological descriptors, and the grouping of cells into phenotype classes according to their descriptors. This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens. Results: We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene's function. Conclusion: Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes.
    Source: Open Access LMU (Universitätsbibliothek der LMU München)
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  • 4
    In: Bioinformatics, 2011, Vol. 27(2), pp.238-244
    Description: Motivation: Targeted interventions in combination with the measurement of secondary effects can be used to computationally reverse engineer features of upstream non-transcriptional signaling cascades. Nested effect models (NEMs) have been introduced as a statistical approach to estimate the upstream signal flow from downstream nested subset structure of perturbation effects. The method was substantially extended later on by several authors and successfully applied to various datasets. The connection of NEMs to Bayesian Networks and factor graph models has been highlighted. Here, we introduce a computationally attractive extension of NEMs that enables the analysis of perturbation time series data, hence allowing to discriminate between direct and indirect signaling and to resolve feedback loops. The implementation (R and C) is part of the Supplement to this article. 〈p〉〈bold〉Contact:〈/bold〉 〈email〉frohlich@bit.uni-bonn.de〈/email〉〈/p〉 are available at online.
    Keywords: Nanoelectromechanical Systems ; Computation ; Mathematical Models ; Upstream ; Perturbation Methods ; Feedback ; Bioinformatics ; Cascades ; Life and Medical Sciences (Ci);
    ISSN: 1367-4803
    E-ISSN: 1460-2059
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  • 5
    In: Bioinformatics, 2010, Vol. 26(18), pp.i596-i602
    Description: Motivation: Network modelling in systems biology has become an important tool to study molecular interactions in cancer research, because understanding the interplay of proteins is necessary for developing novel drugs and therapies. De novo reconstruction of signalling pathways from data allows to unravel interactions between proteins and make qualitative statements on possible aberrations of the cellular regulatory program. We present a new method for reconstructing signalling networks from time course experiments after external perturbation and show an application of the method to data measuring abundance of phosphorylated proteins in a human breast cancer cell line, generated on reverse phase protein arrays. Signalling dynamics is modelled using active and passive states for each protein at each timepoint. A fixed signal propagation scheme generates a set of possible state transitions on a discrete timescale for a given network hypothesis, reducing the number of theoretically reachable states. A likelihood score is proposed, describing the probability of measurements given the states of the proteins over time. The optimal sequence of state transitions is found via a hidden Markov model and network structure search is performed using a genetic algorithm that optimizes the overall likelihood of a population of candidate networks. Our method shows increased performance compared with two different dynamical Bayesian network approaches. For our real data, we were able to find several known signalling cascades from the ERBB signalling pathway. Dynamic deterministic effects propagation networks is implemented in the R programming language and available at 〈p〉〈bold〉Contact:〈/bold〉 〈email〉c.bender@dkfz.de〈/email〉〈/p〉
    Keywords: Learning ; Data Processing ; Bayesian Analysis ; Algorithms ; Erbb Protein ; Computer Programs ; Tumor Cell Lines ; Hidden Markov Models ; Protein Arrays ; Breast Cancer ; Language ; Bioinformatics ; Signal Transduction ; Bioinformatics & Computer Applications;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
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  • 6
    In: Bioinformatics, 2010, Vol. 26(17), pp.2136-2144
    Description: Motivation: One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures. We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Googleʼs PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer dataset comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes. The R code of the proposed algorithm is given in . ; are available at online.
    Keywords: Biology;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
    E-ISSN: 13674811
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  • 7
    Language: English
    In: BMC bioinformatics, 15 October 2007, Vol.8, pp.386
    Description: The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-alpha pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package nem, which can be obtained from the Bioconductor homepage.
    Keywords: Gene Regulatory Networks -- Physiology ; Oligonucleotide Array Sequence Analysis -- Methods ; RNA Interference -- Physiology ; Signal Transduction -- Physiology
    E-ISSN: 1471-2105
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  • 8
    Language: English
    In: Nature communications, 27 April 2016, Vol.7, pp.11420
    Description: Brown adipose tissue (BAT) dissipates energy and its activity correlates with leanness in human adults. (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography coupled with computer tomography (PET/CT) is still the standard for measuring BAT activity, but exposes subjects to ionizing radiation. To study BAT function in large human cohorts, novel diagnostic tools are needed. Here we show that brown adipocytes release exosomes and that BAT activation increases exosome release. Profiling miRNAs in exosomes released from brown adipocytes, and in exosomes isolated from mouse serum, we show that levels of miRNAs change after BAT activation in vitro and in vivo. One of these exosomal miRNAs, miR-92a, is also present in human serum exosomes. Importantly, serum concentrations of exosomal miR-92a inversely correlate with human BAT activity measured by (18)F-FDG PET/CT in two unique and independent cohorts comprising 41 healthy individuals. Thus, exosomal miR-92a represents a potential serum biomarker for BAT activity in mice and humans.
    Keywords: Adipose Tissue, Brown -- Metabolism ; Exosomes -- Metabolism ; Micrornas -- Blood
    E-ISSN: 2041-1723
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  • 9
    Language: English
    In: EURASIP journal on bioinformatics & systems biology, 2009, pp.195272
    Description: Nested effects models (NEMs) are a class of probabilistic models that were designed to reconstruct a hidden signalling structure from a large set of observable effects caused by active interventions into the signalling pathway. We give a more flexible formulation of NEMs in the language of Bayesian networks. Our framework constitutes a natural generalization of the original NEM model, since it explicitly states the assumptions that are tacitly underlying the original version. Our approach gives rise to new learning methods for NEMs, which have been implemented in the R/Bioconductor package nem. We validate these methods in a simulation study and apply them to a synthetic lethality dataset in yeast.
    Keywords: Research Article;
    ISSN: 1687-4145
    E-ISSN: 16874153
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  • 10
    Language: English
    In: BMC Systems Biology, 2014, Vol.8, pp.urn:issn:1752-0509
    Keywords: Tgf-Beta ; Microarray ; Time-Course Analysis ; Gene Set Analysis ; Clustering ; Functional Similarity
    ISSN: 1752-0509
    Source: NARCIS (National Academic Research and Collaborations Information System)
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