pSTIING: a 'systems' approach towards integrating signalling pathways, interaction and transcriptional regulatory networks in inflammation and cancer - PubMed (original) (raw)

pSTIING: a 'systems' approach towards integrating signalling pathways, interaction and transcriptional regulatory networks in inflammation and cancer

Aylwin Ng et al. Nucleic Acids Res. 2006.

Abstract

pSTIING (http://pstiing.licr.org) is a new publicly accessible web-based application and knowledgebase featuring 65 228 distinct molecular associations (comprising protein-protein, protein-lipid, protein-small molecule interactions and transcriptional regulatory associations), ligand-receptor-cell type information and signal transduction modules. It has a particular major focus on regulatory networks relevant to chronic inflammation, cell migration and cancer. The web application and interface provide graphical representations of networks allowing users to combine and extend transcriptional regulatory and signalling modules, infer molecular interactions across species and explore networks via protein domains/motifs, gene ontology annotations and human diseases. pSTIING also supports the direct cross-correlation of experimental results with interaction information in the knowledgebase via the CLADIST tool associated with pSTIING, which currently analyses and clusters gene expression, proteomic and phenotypic datasets. This allows the contextual projection of co-expression patterns onto prior network information, facilitating the identification of functional modules in physiologically relevant systems.

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Figures

Figure 1

Figure 1

Graphical maps generated in pSTIING showing (A) cross-connectivity between four intracellular signalling modules (TLR, IFN-α/β, IFN-γ and Chemokine) and two transcriptional regulatory modules (STAT1 and IRF3). Interactions are represented by solid edges while transcriptional associations by dashed lines. Each component node (e.g. IRAK1) is linked to more extensive information about the component (including interaction partners, InterPro domains, GO, homologous proteins and cross-links to external databases). Clicking on an interaction node provides interaction details and experimental evidence supporting that interaction. Pathways can be extended in a desired direction by displaying subsequent interaction neighbourhood levels centered on interactors selected by users (e.g. mitogen-activated kinase kinases, MAP2K3 and MAP2K6). (B) Representation of ligand–receptor–cell type associations. The ligand CC-chemokine CCL5 activates a subset of leukocytes and lymphocytes by binding to chemokine receptors CCR1, CCR3 and CCR5 expressed on these cell types. Each ligand and receptor node is linked to detailed information about the respective protein and their interaction partners. Information about each cell type, its related or parental cell types and the cell surface markers (CD antigens) that phenotypically identifies the cell type can be accessed by clicking on the cell type node.

Figure 2

Figure 2

(A) The identification of potential orthologues using human-model organism reciprocal similarity (BLASTP) searches allows additional interaction information to be projected from model organism proteins onto orthologous proteins in human. The sequence similarity between orthologous proteins (percent identity, BLAST score and _E_-value) for each query protein is displayed alongside their interaction information and when consolidated across species, these interactions are mapped onto human proteins. As a comparison, the corresponding interaction map without orthology mapping is shown. (B) pSTIING supports querying by human disease, facilitating the exploration of interaction and transcriptional regulatory associations between proteins implicated in disease (e.g. breast cancer). (C) Linking of expression analysis to molecular interaction networks (represented by solid edges) and transcriptional regulatory modules (dashed lines) using pSTIING-CLADIST. The CLADIST tool associated with pSTIING provides various clustering algorithms including SOM, which was used to identify a cluster of co-ordinately expressed genes (represented by nodes with red labels) from MPSS expression analyses of tumour and non-tumour samples. These were then cross-correlated directly with interaction and transcriptional information in pSTIING to detect any underlying physical or functional associations.

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