12 research outputs found

    TranscriptomeBrowser: A Powerful and Flexible Toolbox to Explore Productively the Transcriptional Landscape of the Gene Expression Omnibus Database

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    International audienceAs public microarray repositories are constantly growing, we are facing the challenge of designing strategies to provide productive access to the available data.\ We used a modified version of the Markov clustering algorithm to systematically extract clusters of co-regulated genes from hundreds of microarray datasets stored in the Gene Expression Omnibus database (n = 1,484). This approach led to the definition of 18,250 transcriptional signatures (TS) that were tested for functional enrichment using the DAVID knowledgebase. Over-representation of functional terms was found in a large proportion of these TS (84%). We developed a JAVA application, TBrowser that comes with an open plug-in architecture and whose interface implements a highly sophisticated search engine supporting several Boolean operators (http://tagc.univ-mrs.fr/tbrowser/). User can search and analyze TS containing a list of identifiers (gene symbols or AffyIDs) or associated with a set of functional terms.\ As proof of principle, TBrowser was used to define breast cancer cell specific genes and to detect chromosomal abnormalities in tumors. Finally, taking advantage of our large collection of transcriptional signatures, we constructed a comprehensive map that summarizes gene-gene co-regulations observed through all the experiments performed on HGU133A Affymetrix platform. We provide evidences that this map can extend our knowledge of cellular signaling pathways

    NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways

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    The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources

    NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways

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    The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources

    The Princeton Protein Orthology Database (P-POD): A Comparative Genomics Analysis Tool for Biologists

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    Many biological databases that provide comparative genomics information and tools are now available on the internet. While certainly quite useful, to our knowledge none of the existing databases combine results from multiple comparative genomics methods with manually curated information from the literature. Here we describe the Princeton Protein Orthology Database (P-POD, http://ortholog.princeton.edu), a user-friendly database system that allows users to find and visualize the phylogenetic relationships among predicted orthologs (based on the OrthoMCL method) to a query gene from any of eight eukaryotic organisms, and to see the orthologs in a wider evolutionary context (based on the Jaccard clustering method). In addition to the phylogenetic information, the database contains experimental results manually collected from the literature that can be compared to the computational analyses, as well as links to relevant human disease and gene information via the OMIM, model organism, and sequence databases. Our aim is for the P-POD resource to be extremely useful to typical experimental biologists wanting to learn more about the evolutionary context of their favorite genes. P-POD is based on the commonly used Generic Model Organism Database (GMOD) schema and can be downloaded in its entirety for installation on one's own system. Thus, bioinformaticians and software developers may also find P-POD useful because they can use the P-POD database infrastructure when developing their own comparative genomics resources and database tools

    Splicing modulation as novel therapeutic strategy against diffuse malignant peritoneal mesothelioma

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    Introduction: Therapeutic options for diffuse malignant peritoneal mesothelioma (DMPM) are limited to surgery and locoregional chemotherapy. Despite improvements in survival rates, patients eventually succumb to disease progression. We investigated splicing deregulation both as molecular prognostic factor and potential novel target in DMPM, while we tested modulators of SF3b complex for antitumor activity. Methods: Tissue-microarrays of 64 DMPM specimens were subjected to immunohistochemical assessment of SF3B1 expression and correlation to clinical outcome. Two primary cell cultures were used for gene expression profiling and in vitro screening of SF3b modulators. Drug-induced splicing alterations affecting downstream cellular pathways were detected through RNA sequencing. Ultimately, we established bioluminescent orthotopic mouse models to test the efficacy of splicing modulation in vivo. Results: Spliceosomal genes are differentially upregulated in DMPM cells compared to normal tissues and high expression of SF3B1 correlated with poor clinical outcome in univariate and multivariate analysis. SF3b modulators (Pladienolide-B, E7107, Meayamycin-B) showed potent cytotoxic activity in vitro with IC50 values in the low nanomolar range. Differential splicing analysis of Pladienolide-B-treated cells revealed abundant alterations of transcripts involved in cell cycle, apoptosis and other oncogenic pathways. This was validated by RT-PCR and functional assays. E7107 demonstrated remarkable in vivo antitumor efficacy, with significant improvement of survival rates compared to vehicle-treated controls. Conclusions: SF3B1 emerged as a novel potential prognostic factor in DMPM. Splicing modulators markedly impair cancer cell viability, resulting also in potent antitumor activity in vivo. Our data designate splicing as a promising therapeutic target in DMPM

    Computational Methods for Knowledge Integration in the Analysis of Large-scale Biological Networks

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    abstract: As we migrate into an era of personalized medicine, understanding how bio-molecules interact with one another to form cellular systems is one of the key focus areas of systems biology. Several challenges such as the dynamic nature of cellular systems, uncertainty due to environmental influences, and the heterogeneity between individual patients render this a difficult task. In the last decade, several algorithms have been proposed to elucidate cellular systems from data, resulting in numerous data-driven hypotheses. However, due to the large number of variables involved in the process, many of which are unknown or not measurable, such computational approaches often lead to a high proportion of false positives. This renders interpretation of the data-driven hypotheses extremely difficult. Consequently, a dismal proportion of these hypotheses are subject to further experimental validation, eventually limiting their potential to augment existing biological knowledge. This dissertation develops a framework of computational methods for the analysis of such data-driven hypotheses leveraging existing biological knowledge. Specifically, I show how biological knowledge can be mapped onto these hypotheses and subsequently augmented through novel hypotheses. Biological hypotheses are learnt in three levels of abstraction -- individual interactions, functional modules and relationships between pathways, corresponding to three complementary aspects of biological systems. The computational methods developed in this dissertation are applied to high throughput cancer data, resulting in novel hypotheses with potentially significant biological impact.Dissertation/ThesisPh.D. Computer Science 201

    Heparan sulfate glycosaminoglycan regulation of vasculogenesis

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    Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 137-154).Neovascularization is an essential process to repair ischemic tissues following myocardial infarction, stroke, diabetic complications, or transplant procedures. Blood vessels are generated by distinct vasculogenic and angiogenic processes. Although multiple proangiogenic factors have been identified, limited success has been achieved translating these as clinical therapeutics. Furthermore, recent studies have shown that vasculogenesis contributes to adult neovascularization in multiple settings. Harnessing the vasculogenic potential of embryonic stem cells is an emerging concept to generate neovasculature. The differentiation of embryonic stem cells into endothelium has been well documented, however most studies focus on genetic or chemokine regulation. Limited information exists which implicates the role of the extracellular microenvironment in stem cell differentiation. Heparan sulfate glycosaminoglycans (HSGAG) are a crucial part of the dynamic extracellular matrix and have been shown to regulate multiple signaling cascades, including vasculogenic specific growth factors VEGF and FGF. The goal of this thesis is to elucidate the role of HSGAG in vasculogenesis. An embryonic stem cell embryoid body model was used to establish the necessity of sulfated HSGAG for endothelial differentiation. We identified that the chemical composition of HSGAG sulfation patterns change with differentiation. Perturbation of HSGAG structure by chemical, enzymatic, or genetic modification effectively inhibited vasculogenesis. Genetic silencing of HSGAG modifying enzyme, N-deacetylase/N-sulfotransferase-1, translated to inhibition of HSGAG sulfation and resulted in impaired blood vessel development in zebrafish embryos. Interestingly, vessel formation in both embryonic stem cell and zebrafish models was restored by the addition of exogenous HSGAG, opening the door for engineering glyco-based microenvironments for controlling vascular development. To explore novel mechanisms of vasculogenesis modulated by HSGAG perturbation, we performed a global transcriptome analysis of N-deacetylase/N-sulfotransferase-1 mutant zebrafish embryos. Several novel pathways were identified that regulate vascular differentiation, including Foxo3A and Insulin-Like Growth Factor (IGF) pathways. We explored the role of IGFs in vasculogenesis specifically and determined for the first time that IGF1 and IGF2 promote mesoderm and endothelial differentiation, mediated through HIFl[alpha] stabilization, in embryonic stem cells. In summary, we've identified several mechanisms by which HSGAG regulate neovascularization, laying the groundwork for incorporating HSGAG in strategies for ischemic tissue regeneration.by Stephanie Marie Piecewicz.Ph.D

    Knowledge derivation and data mining strategies for probabilistic functional integrated networks

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    PhDOne of the fundamental goals of systems biology is the experimental verification of the interactome: the entire complement of molecular interactions occurring in the cell. Vast amounts of high-throughput data have been produced to aid this effort. However these data are incomplete and contain high levels of both false positives and false negatives. In order to combat these limitations in data quality, computational techniques have been developed to evaluate the datasets and integrate them in a systematic fashion using graph theory. The result is an integrated network which can be analysed using a variety of network analysis techniques to draw new inferences about biological questions and to guide laboratory experiments. Individual research groups are interested in specific biological problems and, consequently, network analyses are normally performed with regard to a specific question. However, the majority of existing data integration techniques are global and do not focus on specific areas of biology. Currently this issue is addressed by using known annotation data (such as that from the Gene Ontology) to produce process-specific subnetworks. However, this approach discards useful information and is of limited use in poorly annotated areas of the interactome. Therefore, there is a need for network integration techniques that produce process-specific networks without loss of data. The work described here addresses this requirement by extending one of the most powerful integration techniques, probabilistic functional integrated networks (PFINs), to incorporate a concept of biological relevance. Initially, the available functional data for the baker’s yeast Saccharomyces cerevisiae was evaluated to identify areas of bias and specificity which could be exploited during network integration. This information was used to develop an integration technique which emphasises interactions relevant to specific biological questions, using yeast ageing as an exemplar. The integration method improves performance during network-based protein functional prediction in relation to this process. Further, the process-relevant networks complement classical network integration techniques and significantly improve network analysis in a wide range of biological processes. The method developed has been used to produce novel predictions for 505 Gene Ontology biological processes. Of these predictions 41,610 are consistent with existing computational annotations, and 906 are consistent with known expert-curated annotations. The approach significantly reduces the hypothesis space for experimental validation of genes hypothesised to be involved in the oxidative stress response. Therefore, incorporation of biological relevance into network integration can significantly improve network analysis with regard to individual biological questions
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