Reactome Knowledgebase of Human Biological Pathways and Processes

Department of Biochemistry, New York University School of Medicine, New York, NY, USA.
Methods in molecular biology (Clifton, N.J.) (Impact Factor: 1.29). 01/2011; 694:49-61. DOI: 10.1007/978-1-60761-977-2_4
Source: PubMed


The Reactome Knowledgebase is an online, manually curated resource that provides an integrated view of the molecular details of human biological processes that range from metabolism to DNA replication and repair to signaling cascades. Its data model allows these diverse processes to be represented in a consistent way to facilitate usage as online text and as a resource for data mining, modeling, and analysis of large-scale expression data sets over the full range of human biological processes.

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    • "These 43 top ranking genes were then selected for enrichment analysis in the Reactome database using the overrepresentation pathway analysis [7]. This algorithm delivered a list of “Statistically over-represented pathways” which represents all Reactome pathways containing proteins from the input gene list. "
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    ABSTRACT: Staphylococcal enterotoxins may influence the pro-inflammatory pattern of chronic sinus diseases via epigenetic events. This work intended to investigate the potential of staphylococcal enterotoxin B (SEB) to induce changes in the DNA methylation pattern. Nasal polyp tissue explants were cultured in the presence and absence of SEB; genomic DNA was then isolated and used for whole genome methylation analysis. Results showed that SEB stimulation altered the methylation pattern of gene regions when compared with non stimulated tissue. Data enrichment analysis highlighted two genes: the IKBKB and STAT-5B, both playing a crucial role in T- cell maturation/activation and immune response.
    Allergy Asthma and Clinical Immunology 12/2013; 9(1):48. DOI:10.1186/1710-1492-9-48 · 2.03 Impact Factor
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    • "DAVID [52], a functional annotation tool, was used to analyze the enriched KEGG [53] and REACTOME [54] pathways with default settings. The integrative network of miRNA-mediated host-influenza virus protein interactions was drawn using Cytoscape [55]. "
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    ABSTRACT: Background With concerns about the disastrous health and economic consequences caused by the influenza pandemic, comprehensively understanding the global host response to influenza virus infection is urgent. The role of microRNA (miRNA) has recently been highlighted in pathogen-host interactions. However, the precise role of miRNAs in the pathogenesis of influenza virus infection in humans, especially in critically ill patients is still unclear. Methods We identified cellular miRNAs involved in the host response to influenza virus infection by performing comprehensive miRNA profiling in peripheral blood mononuclear cells (PBMCs) from critically ill patients with swine-origin influenza pandemic H1N1 (2009) virus infection via miRNA microarray and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) assays. Receiver operator characteristic (ROC) curve analysis was conducted and area under the ROC curve (AUC) was calculated to evaluate the diagnostic accuracy of severe H1N1 influenza virus infection. Furthermore, an integrative network of miRNA-mediated host-influenza virus protein interactions was constructed by integrating the predicted and validated miRNA-gene interaction data with influenza virus and host-protein-protein interaction information using Cytoscape software. Moreover, several hub genes in the network were selected and validated by qRT-PCR. Results Forty-one significantly differentially expressed miRNAs were found by miRNA microarray; nine were selected and validated by qRT-PCR. QRT-PCR assay and ROC curve analyses revealed that miR-31, miR-29a and miR-148a all had significant potential diagnostic value for critically ill patients infected with H1N1 influenza virus, which yielded AUC of 0.9510, 0.8951 and 0.8811, respectively. We subsequently constructed an integrative network of miRNA-mediated host-influenza virus protein interactions, wherein we found that miRNAs are involved in regulating important pathways, such as mitogen-activated protein kinase signaling pathway, epidermal growth factor receptor signaling pathway, and Toll-like receptor signaling pathway, during influenza virus infection. Some of differentially expressed miRNAs via in silico analysis targeted mRNAs of several key genes in these pathways. The mRNA expression level of tumor protein T53 and transforming growth factor beta receptor 1 were found significantly reduced in critically ill patients, whereas the expression of Janus kinase 2, caspase 3 apoptosis-related cysteine peptidase, interleukin 10, and myxovirus resistance 1 were extremely increased in critically ill patients. Conclusions Our data suggest that the dysregulation of miRNAs in the PBMCs of H1N1 critically ill patients can regulate a number of key genes in the major signaling pathways associated with influenza virus infection. These differentially expressed miRNAs could be potential therapeutic targets or biomarkers for severe influenza virus infection.
    BMC Infectious Diseases 06/2013; 13(1):257. DOI:10.1186/1471-2334-13-257 · 2.61 Impact Factor
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    • "For a comprehensive review on network terminology, and various ways to analyse networks please refer to the work of Sanz- Pamplona et al. [55]. One common approach when studying cancer datasets is to work with characterised pathways/networks – such as signalling and metabolic networks as defined in databases such as KEGG [56] [57] and Reactome [58]. Some previous studies have used such networks to identify which of these curated pathways may be deregulated in specific cancers [3] [59]. "
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    ABSTRACT: Over recent years, with the advances in next-generation sequencing, a large number of cancer mutations have been identified and accumulated in public repositories. Coupled to this is our increased ability to generate detailed interactome maps that help to enrich our knowledge of the biological implications of cancer mutations. As a result, network analysis approaches have become an invaluable tool to predict and interpret mutations that are associated with tumour survival and progression. Our understanding of cancer mechanisms is further enhanced by mapping protein structure information to such networks. Here we review the current methodologies for annotating the functional impacts of cancer mutations, which range from analysis of protein structures to protein-protein interaction network studies.
    Seminars in Cancer Biology 05/2013; 23(4). DOI:10.1016/j.semcancer.2013.05.002 · 9.33 Impact Factor
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