Integration of biological networks and gene expression data using Cytoscape.

Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.

Network analysis of human in-stent restenosis.

BACKGROUND: Recent successes in the treatment of in-stent restenosis (ISR) by drug-eluting stents belie the challenges still faced in certain lesions and patient groups. We analyzed human coronary atheroma in de novo and restenotic disease to identify targets of therapy that might avoid these limitations. METHODS AND RESULTS: We recruited 89 patients who underwent coronary atherectomy for de novo atherosclerosis (n=55) or in-stent restenosis (ISR) of a bare metal stent (n=34). Samples were fixed for histology, and gene expression was assessed with a dual-dye 22,000 oligonucleotide microarray. Histological analysis revealed significantly greater cellularity and significantly fewer inflammatory infiltrates and lipid pools in the ISR group. Gene ontology analysis demonstrated the prominence of cell proliferation programs in ISR and inflammation/immune programs in de novo restenosis. Network analysis, which combines semantic mining of the published literature with the expression signature of ISR, revealed gene expression modules suggested as candidates for selective inhibition of restenotic disease. Two modules are presented in more detail, the procollagen type 1 alpha2 gene and the ADAM17/tumor necrosis factor-alpha converting enzyme gene. We tested our contention that this method is capable of identifying successful targets of therapy by comparing mean significance scores for networks generated from subsets of the published literature containing the terms "sirolimus" or "paclitaxel." In addition, we generated 2 large networks with sirolimus and paclitaxel at their centers. Both analyses revealed higher mean values for sirolimus, suggesting that this agent has a broader suppressive action against ISR than paclitaxel. CONCLUSIONS: Comprehensive histological and gene network analysis of human ISR reveals potential targets for directed abrogation of restenotic disease and recapitulates the results of clinical trials of existing agents.

Genetic variation in putative regulatory loci controlling gene expression in breast cancer.

Candidate single-nucleotide polymorphisms (SNPs) were analyzed for associations to an unselected whole genome pool of tumor mRNA transcripts in 50 unrelated patients with breast cancer. SNPs were selected from 203 candidate genes of the reactive oxygen species pathway. We describe a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort, which revealed significant associations between subsets of SNPs and transcripts, shedding light on the underlying biology. We identified SNPs in EGF, IL1A, MAPK8, XPC, SOD2, and ALOX12 that are associated with the expression patterns of a significant number of transcripts, indicating the presence of regulatory SNPs in these genes. SNPs were found to act in trans in a total of 115 genes. SNPs in 43 of these 115 genes were found to act both in cis and in trans. Finally, subsets of SNPs that share significantly many common associations with a set of transcripts (biclusters) were identified. The subsets of transcripts that are significantly associated with the same set of SNPs or to a single SNP were shown to be functionally coherent in Gene Ontology and pathway analyses and coexpressed in other independent data sets, suggesting that many of the observed associations are within the same functional pathways. To our knowledge, this article is the first study to correlate SNP genotype data in the germ line with somatic gene expression data in breast tumors. It provides the statistical framework for further genotype expression correlation studies in cancer data sets.

Molecular signatures determining coronary artery and saphenous vein smooth muscle cell phenotypes: distinct responses to stimuli.

OBJECTIVE: Phenotypic differences between vascular smooth muscle cell (VSMC) subtypes lead to diverse pathological processes including atherosclerosis, postangioplasty restenosis and vein graft disease. To better understand the molecular mechanisms underlying functional differences among distinct SMC subtypes, we compared gene expression profiles and functional responses to oxidized low-density lipoprotein (OxLDL) and platelet-derived growth factor (PDGF) between cultured SMCs from human coronary artery (CASM) and saphenous vein (SVSM). METHODS AND RESULTS: OxLDL and PDGF elicited markedly different functional responses and expression profiles between the 2 SMC subtypes. In CASM, OxLDL inhibited cell proliferation and migration and modified gene expression of chemokines (CXCL10, CXCL11 and CXCL12), proinflammatory cytokines (IL-1, IL-6, and IL-18), insulin-like growth factor binding proteins (IGFBPs), and both endothelial and smooth muscle marker genes. In SVSM, OxLDL promoted proliferation partially via IGF1 signaling, activated NF-kappaB and phosphatidylinositol signaling pathways, and upregulated prostaglandin (PG) receptors and synthases. In untreated cells, alpha-chemokines, proinflammatory cytokines, and genes associated with apoptosis, inflammation, and lipid biosynthesis were higher in CASM, whereas some beta-chemokines, metalloproteinase inhibitors, and IGFBPs were higher in SVSM. Interestingly, the basal expression levels of these genes seemed closely related to their responses to OxLDL and PDGF. In summary, our results suggest dramatic differences in gene expression patterns and functional responses to OxLDL and PDGF between venous and arterial SMCs, with venous SMCs having stronger proliferative/migratory responses to stimuli but also higher expression of atheroprotective genes at baseline. CONCLUSIONS: These results reveal molecular signatures that define the distinct phenotypes characteristics of coronary artery and saphenous vein SMC subtypes.

Differences in vascular bed disease susceptibility reflect differences in gene expression response to atherogenic stimuli.

Atherosclerosis occurs predominantly in arteries and only rarely in veins. The goal of this study was to test whether differences in the molecular responses of venous and arterial endothelial cells (ECs) to atherosclerotic stimuli might contribute to vascular bed differences in susceptibility to atherosclerosis. We compared gene expression profiles of primary cultured ECs from human saphenous vein (SVEC) and coronary artery (CAEC) exposed to atherogenic stimuli. In addition to identifying differentially expressed genes, we applied statistical analysis of gene ontology and pathway annotation terms to identify signaling differences related to cell type and stimulus. Differential gene expression of untreated venous and arterial endothelial cells yielded 285 genes more highly expressed in untreated SVEC (P<0.005 and fold change >1.5). These genes represented various atherosclerosis-related pathways including responses to proliferation, oxidoreductase activity, antiinflammatory responses, cell growth, and hemostasis functions. Moreover, stimulation with oxidized LDL induced dramatically greater gene expression responses in CAEC compared with SVEC, relating to adhesion, proliferation, and apoptosis pathways. In contrast, interleukin 1beta and tumor necrosis factor alpha activated similar gene expression responses in both CAEC and SVEC. The differences in functional response and gene expression were further validated by an in vitro proliferation assay and in vivo immunostaining of alphabeta-crystallin protein. Our results strongly suggest that different inherent gene expression programs in arterial versus venous endothelial cells contribute to differences in atherosclerotic disease susceptibility.

Pathway analysis of coronary atherosclerosis.

Large-scale gene expression studies provide significant insight into genes differentially regulated in disease processes such as cancer. However, these investigations offer limited understanding of multisystem, multicellular diseases such as atherosclerosis. A systems biology approach that accounts for gene interactions, incorporates nontranscriptionally regulated genes, and integrates prior knowledge offers many advantages. We performed a comprehensive gene level assessment of coronary atherosclerosis using 51 coronary artery segments isolated from the explanted hearts of 22 cardiac transplant patients. After histological grading of vascular segments according to American Heart Association guidelines, isolated RNA was hybridized onto a customized 22-K oligonucleotide microarray, and significance analysis of microarrays and gene ontology analyses were performed to identify significant gene expression profiles. Our studies revealed that loss of differentiated smooth muscle cell gene expression is the primary expression signature of disease progression in atherosclerosis. Furthermore, we provide insight into the severe form of coronary artery disease associated with diabetes, reporting an overabundance of immune and inflammatory signals in diabetics. We present a novel approach to pathway development based on connectivity, determined by language parsing of the published literature, and ranking, determined by the significance of differentially regulated genes in the network. In doing this, we identify highly connected "nexus" genes that are attractive candidates for therapeutic targeting and followup studies. Our use of pathway techniques to study atherosclerosis as an integrated network of gene interactions expands on traditional microarray analysis methods and emphasizes the significant advantages of a systems-based approach to analyzing complex disease.

An architecture for biological information extraction and representation.

MOTIVATIONS: Technological advances in biomedical research are generating a plethora of heterogeneous data at a high rate. There is a critical need for extraction, integration and management tools for information discovery and synthesis from these heterogeneous data. RESULTS: In this paper, we present a general architecture, called ALFA, for information extraction and representation from diverse biological data. The ALFA architecture consists of: (i) a networked, hierarchical, hyper-graph object model for representing information from heterogeneous data sources in a standardized, structured format; and (ii) a suite of integrated, interactive software tools for information extraction and representation from diverse biological data sources. As part of our research efforts to explore this space, we have currently prototyped the ALFA object model and a set of interactive software tools for searching, filtering, and extracting information from scientific text. In particular, we describe BioFerret, a meta-search tool for searching and filtering relevant information from the web, and ALFA Text Viewer, an interactive tool for user-guided extraction, disambiguation, and representation of information from scientific text. We further demonstrate the potential of our tools in integrating the extracted information with experimental data and diagrammatic biological models via the common underlying ALFA representation. CONTACT: aditya_vailaya@agilent.com.

Automatic image orientation detection.

We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.