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Schaefer M.H.,Max Delbruck Center for Molecular Medicine | Lopes T.J.S.,JST ERATO KAWAOKA Infection induced Host Responses Project | Mah N.,Max Delbruck Center for Molecular Medicine | Shoemaker J.E.,JST ERATO KAWAOKA Infection induced Host Responses Project | And 15 more authors.
PLoS Computational Biology | Year: 2013

Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways. © 2013 Schaefer et al. Source

Shoemaker J.E.,JST ERATO KAWAOKA Infection induced Host Responses Project | Lopes T.J.S.,JST ERATO KAWAOKA Infection induced Host Responses Project | Ghosh S.,The Systems Biology Institute | Matsuoka Y.,JST ERATO KAWAOKA Infection induced Host Responses Project | And 8 more authors.
BMC Genomics | Year: 2012

Background: Interpreting in vivo sampled microarray data is often complicated by changes in the cell population demographics. To put gene expression into its proper biological context, it is necessary to distinguish differential gene transcription from artificial gene expression induced by changes in the cellular demographics.Results: CTen (cell type enrichment) is a web-based analytical tool which uses our highly expressed, cell specific (HECS) gene database to identify enriched cell types in heterogeneous microarray data. The web interface is designed for differential expression and gene clustering studies, and the enrichment results are presented as heatmaps or downloadable text files.Conclusions: In this work, we use an independent, cell-specific gene expression data set to assess CTen's performance in accurately identifying the appropriate cell type and provide insight into the suggested level of enrichment to optimally minimize the number of false discoveries. We show that CTen, when applied to microarray data developed from infected lung tissue, can correctly identify the cell signatures of key lymphocytes in a highly heterogeneous environment and compare its performance to another popular bioinformatics tool. Furthermore, we discuss the strong implications cell type enrichment has in the design of effective microarray workflow strategies and show that, by combining CTen with gene expression clustering, we may be able to determine the relative changes in the number of key cell types.CTen is available at http://www.influenza-x.org/~jshoemaker/cten/. © 2012 Shoemaker et al.; licensee BioMed Central Ltd. Source

Matsuoka Y.,JST ERATO KAWAOKA Infection induced Host Responses Project | Matsuoka Y.,The Systems Biology Institute | Matsumae H.,Tokyo Medical and Dental University | Katoh M.,JST ERATO KAWAOKA Infection induced Host Responses Project | And 19 more authors.
BMC Systems Biology | Year: 2013

Background: Influenza is a common infectious disease caused by influenza viruses. Annual epidemics cause severe illnesses, deaths, and economic loss around the world. To better defend against influenza viral infection, it is essential to understand its mechanisms and associated host responses. Many studies have been conducted to elucidate these mechanisms, however, the overall picture remains incompletely understood. A systematic understanding of influenza viral infection in host cells is needed to facilitate the identification of influential host response mechanisms and potential drug targets.Description: We constructed a comprehensive map of the influenza A virus ('IAV') life cycle ('FluMap') by undertaking a literature-based, manual curation approach. Based on information obtained from publicly available pathway databases, updated with literature-based information and input from expert virologists and immunologists, FluMap is currently composed of 960 factors (i.e., proteins, mRNAs etc.) and 456 reactions, and is annotated with ~500 papers and curation comments. In addition to detailing the type of molecular interactions, isolate/strain specific data are also available. The FluMap was built with the pathway editor CellDesigner in standard SBML (Systems Biology Markup Language) format and visualized as an SBGN (Systems Biology Graphical Notation) diagram. It is also available as a web service (online map) based on the iPathways+ system to enable community discussion by influenza researchers. We also demonstrate computational network analyses to identify targets using the FluMap.Conclusion: The FluMap is a comprehensive pathway map that can serve as a graphically presented knowledge-base and as a platform to analyze functional interactions between IAV and host factors. Publicly available webtools will allow continuous updating to ensure the most reliable representation of the host-virus interaction network. The FluMap is available at http://www.influenza-x.org/flumap/. © 2013 Matsuoka et al.; licensee BioMed Central Ltd. Source

Lopes T.J.S.,JST ERATO KAWAOKA Infection induced Host Responses Project | Schaefer M.,Max Delbruck Center for Molecular Medicine | Shoemaker J.,JST ERATO KAWAOKA Infection induced Host Responses Project | Matsuoka Y.,JST ERATO KAWAOKA Infection induced Host Responses Project | And 11 more authors.
Bioinformatics | Year: 2011

Motivation: Protein-protein interaction (PPI) databases are widely used tools to study cellular pathways and networks; however, there are several databases available that still do not account for cell typespecific differences. Here, we evaluated the characteristics of six interaction databases, incorporated tissue-specific gene expression information and finally, investigated if the most popular proteins of scientific literature are involved in good quality interactions. Results: We found that the evaluated databases are comparable in terms of node connectivity (i.e. proteins with few interaction partners also have few interaction partners in other databases), but may differ in the identity of interaction partners. We also observed that the incorporation of tissue-specific expression information significantly altered the interaction landscape and finally, we demonstrated that many of the most intensively studied proteins are engaged in interactions associated with low confidence scores. In summary, interaction databases are valuable research tools but may lead to different predictions on interactions or pathways. The accuracy of predictions can be improved by incorporating datasets on organand cell type-specific gene expression, and by obtaining additional interaction evidence for the most 'popular' proteins. © The Author 2011. Published by Oxford University Press. All rights reserved. Source

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