Composite network of molecular pathways active in HCV-i

Tools enabling the elucidation of molecular pathways active in human disease: application to Hepatitis C virus infection. Reiss DJ, Avila-Campillo I, Thorsson V, Schwikowski B, Galitski T - BMC Bioinformatics (2005) Bottom Line: The extraction of biological knowledge from genome-scale data sets requires its analysis in the context of additional biological information.The importance of integrating experimental data sets with molecular interaction networks has been recognized and applied to the study of model organisms, but its systematic application to the study of human disease has lagged behind due to the lack of tools for performing such integration.We have developed techniques and software tools for simplifying and streamlining the process of integration of diverse experimental data types in molecular networks, as well as for the analysis of these networks. View Article: PubMed Central - HTML - PubMed Affiliation: Institute for Systems Biology, 1441 N, 34th Street, Seattle, WA 98103, USA. dreiss@systemsbiology.org ABSTRACT Background: The extraction of biological knowledge from genome-scale data sets requires its analysis in the context of additional biological information. The importance of integrating experimental data sets with molecular interaction networks has been recognized and applied to the study of model organisms, but its systematic application to the study of human disease has lagged behind due to the lack of tools for performing such integration. Results: We have developed techniques and software tools for simplifying and streamlining the process of integration of diverse experimental data types in molecular networks, as well as for the analysis of these networks. We applied these techniques to extract, from genomic expression data from Hepatitis C virus-infected liver tissue, potentially useful hypotheses related to the onset of this disease. Our integration of the expression data with large-scale molecular interaction networks and subsequent analyses identified molecular pathways that appear to be induced or repressed in the response to Hepatitis C viral infection. Conclusion: The methods and tools we have implemented allow for the efficient dynamic integration and analysis of diverse data in a major human disease system. This integrated data set in turn enabled simple analyses to yield hypotheses related to the response to Hepatitis C viral infection. Show MeSH Major Computational Biology/methods* Hepatitis C/genetics/metabolism Oligonucleotide Array Sequence Analysis/methods* Protein Interaction Mapping/methods* Minor Databases, Protein Gene Expression Profiling Genome Hepacivirus/metabolism Humans Internet Liver/virology Models, Biological Models, Theoretical Programming Languages Proteome Software Related in: MedlinePlus	© Copyright Policy - open-access Related In: Results - Collection License Show All Figures getmorefigures.php?uid=PMC1181626&req=5 Figure 3: Composite network of molecular pathways active in HCV-infected liver tissue. The network in Figure 1 was combined with active subnetworks from the network in Figure 2. The active subnetworks were identified by active-paths analysis ([5]; bold nodes and edges) and by identifying the subnetworks that changed most significantly in expression with time after transplant. Nodes (genes) colored red were induced in the expression data of biopsies from 8 months or more post-transplant; green nodes were repressed. Areas that contain differentially active pathways or subnetworks, as described in the text, are highlighted. Mentions: In Figure 3, we have integrated the seed network, the composite active-paths network, and the "only-late-active" network into one network. This network is available for exploration and analysis in Cytoscape at our laboratory web site [23]. Genes that were induced on average after 8 months following transplant are indicated with a red colour. Genes that were repressed are green. We have highlighted the nodes and edges of the composite active-paths subnetwork in "bold". The network in Figure 3 is significantly over-represented with genes of several biological processes, as annotated by the Gene Ontology Consortium Database [34,35]; using the BioDataServer tool in Cytoscape, and computed in R via CytoTalk, using the Bonferroni-corrected hypergeometric distribution. Among these include blood coagulation (p = 10-11), immune response (p = 10-7), proteolysis and peptidolysis (p = 10-5), lipid transport (p = 10-3), and complement activation (p = 10-2). In addition, nearly the entire JAK-STAT interferon-response signalling pathway is activated in this network.

Tools enabling the elucidation of molecular pathways active in human disease: application to Hepatitis C virus infection.

Reiss DJ, Avila-Campillo I, Thorsson V, Schwikowski B, Galitski T - BMC Bioinformatics (2005)

Bottom Line: The extraction of biological knowledge from genome-scale data sets requires its analysis in the context of additional biological information.The importance of integrating experimental data sets with molecular interaction networks has been recognized and applied to the study of model organisms, but its systematic application to the study of human disease has lagged behind due to the lack of tools for performing such integration.We have developed techniques and software tools for simplifying and streamlining the process of integration of diverse experimental data types in molecular networks, as well as for the analysis of these networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Systems Biology, 1441 N, 34th Street, Seattle, WA 98103, USA. dreiss@systemsbiology.org

ABSTRACT

Background: The extraction of biological knowledge from genome-scale data sets requires its analysis in the context of additional biological information. The importance of integrating experimental data sets with molecular interaction networks has been recognized and applied to the study of model organisms, but its systematic application to the study of human disease has lagged behind due to the lack of tools for performing such integration.

Results: We have developed techniques and software tools for simplifying and streamlining the process of integration of diverse experimental data types in molecular networks, as well as for the analysis of these networks. We applied these techniques to extract, from genomic expression data from Hepatitis C virus-infected liver tissue, potentially useful hypotheses related to the onset of this disease. Our integration of the expression data with large-scale molecular interaction networks and subsequent analyses identified molecular pathways that appear to be induced or repressed in the response to Hepatitis C viral infection.

Conclusion: The methods and tools we have implemented allow for the efficient dynamic integration and analysis of diverse data in a major human disease system. This integrated data set in turn enabled simple analyses to yield hypotheses related to the response to Hepatitis C viral infection.

Show MeSH

Related in: MedlinePlus

Composite network of molecular pathways active in HCV-infected liver tissue. The network in Figure 1 was combined with active subnetworks from the network in Figure 2. The active subnetworks were identified by active-paths analysis ([5]; bold nodes and edges) and by identifying the subnetworks that changed most significantly in expression with time after transplant. Nodes (genes) colored red were induced in the expression data of biopsies from 8 months or more post-transplant; green nodes were repressed. Areas that contain differentially active pathways or subnetworks, as described in the text, are highlighted.

Related In: Results - Collection

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Figure 3: Composite network of molecular pathways active in HCV-infected liver tissue. The network in Figure 1 was combined with active subnetworks from the network in Figure 2. The active subnetworks were identified by active-paths analysis ([5]; bold nodes and edges) and by identifying the subnetworks that changed most significantly in expression with time after transplant. Nodes (genes) colored red were induced in the expression data of biopsies from 8 months or more post-transplant; green nodes were repressed. Areas that contain differentially active pathways or subnetworks, as described in the text, are highlighted.

Mentions: In Figure 3, we have integrated the seed network, the composite active-paths network, and the "only-late-active" network into one network. This network is available for exploration and analysis in Cytoscape at our laboratory web site [23]. Genes that were induced on average after 8 months following transplant are indicated with a red colour. Genes that were repressed are green. We have highlighted the nodes and edges of the composite active-paths subnetwork in "bold". The network in Figure 3 is significantly over-represented with genes of several biological processes, as annotated by the Gene Ontology Consortium Database [34,35]; using the BioDataServer tool in Cytoscape, and computed in R via CytoTalk, using the Bonferroni-corrected hypergeometric distribution. Among these include blood coagulation (p = 10-11), immune response (p = 10-7), proteolysis and peptidolysis (p = 10-5), lipid transport (p = 10-3), and complement activation (p = 10-2). In addition, nearly the entire JAK-STAT interferon-response signalling pathway is activated in this network.