GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop

The GeneMANIA Cytoscape plugin brings fast gene function prediction capabilities to the desktop. GeneMANIA identifies the most related genes to a query gene set using a guilt-by-association approach. The plugin uses over 800 networks from six organisms and each related gene is traceable to the source network used to make the prediction. Users may add their own interaction networks and expression profile data to complement or override the default data. Availability and Implementation: The GeneMANIA Cytoscape plugin is implemented in Java and is freely available at http://www.genemania.org/plugin/.     

Global analysis of G-protein-coupled receptor signaling in human tissues

The G-protein-coupled receptor (GPCR) family mediates a host of cell–cell communications upon activation by diverse ligands. Numerous GPCRs have been shown to display anatomically selective patterns of gene expression, however, our understanding of the complexity of GPCR signaling within human tissues remains unclear. In an effort to characterize global patterns of GPCR signaling in the human body, microarray analysis was performed on a large panel of tissues to monitor the gene expression levels of the receptors as well as related signaling and regulatory molecules. Analysis of the data revealed complex signaling networks in many tissue types, with tissue-specific patterns of gene expression observed for the majority of the receptors and a number of components and regulators of GPCR signaling.     

PANET: A GPU-Based Tool for Fast Parallel Analysis of Robustness Dynamics and Feed-Forward/Feedback Loop Structures in Large-Scale Biological Networks

It has been a challenge in systems biology to unravel relationships between structural properties and dynamic behaviors of biological networks. A Cytoscape plugin named NetDS was recently proposed to analyze the robustness-related dynamics and feed-forward/feedback loop structures of biological networks. Despite such a useful function, limitations on the network size that can be analyzed exist due to high computational costs. In addition, the plugin cannot verify an intrinsic property which can be induced by an observed result because it has no function to simulate the observation on a large number of random networks. To overcome these limitations, we have developed a novel software tool, PANET. First, the time-consuming parts of NetDS were redesigned to be processed in parallel using the OpenCL library. This approach utilizes the full computing power of multi-core central processing units and graphics processing units. Eventually, this made it possible to investigate a large-scale network such as a human signaling network with 1,609 nodes and 5,063 links. We also developed a new function to perform a batch-mode simulation where it generates a lot of random networks and conducts robustness calculations and feed-forward/feedback loop examinations of them. This helps us to determine if the findings in real biological networks are valid in arbitrary random networks or not. We tested our plugin in two case studies based on two large-scale signaling networks and found interesting results regarding relationships between coherently coupled feed-forward/feedback loops and robustness. In addition, we verified whether or not those findings are consistently conserved in random networks through batch-mode simulations. Taken together, our plugin is expected to effectively investigate various relationships between dynamics and structural properties in large-scale networks. Our software tool, user manual and example datasets are freely available at http://panet-csc.sourceforge.net/.     

A tissue-specific atlas of mouse protein phosphorylation and expression

Although most tissues in an organism are genetically identical, the biochemistry of each is optimized to fulfill its unique physiological roles, with important consequences for human health and disease. Each?tissue's unique physiology requires tightly regulated gene and protein expression coordinated by specialized, phosphorylation-dependent intracellular signaling. To better understand the role of phosphorylation in maintenance of physiological differences among tissues, we performed proteomic and phosphoproteomic characterizations of nine mouse tissues. We identified 12,039 proteins, including 6296 phosphoproteins harboring nearly 36,000 phosphorylation sites. Comparing protein abundances and phosphorylation levels revealed specialized, interconnected phosphorylation networks within each tissue while suggesting that many proteins are regulated by phosphorylation independently of their expression. Our data suggest that the "typical" phosphoprotein is widely expressed yet displays variable, often tissue-specific phosphorylation that tunes protein activity to the specific needs of each tissue. We offer this dataset as an online resource for the biological research community.     

Construction and Analyses of Human Large-Scale Tissue Specific Networks

Construction and analyses of tissue specific networks is crucial to unveil the function and organizational structure of biological systems. As a direct method to detect protein dynamics, human proteome-wide expression data provide an valuable resource to investigate the tissue specificity of proteins and interactions. By integrating protein expression data with large-scale interaction network, we constructed 30 tissue/cell specific networks in human and analyzed their properties and functions. Rather than the tissue specificity of proteins, we mainly focused on the tissue specificity of interactions to distill tissue specific networks. Through comparing our tissue specific networks with those inferred from gene expression data, we found our networks have larger scales and higher reliability. Furthermore, we investigated the similar extent of multiple tissue specific networks, which proved that tissues with similar functions tend to contain more common interactions. Finally, we found that the tissue specific networks differed from the static network in multiple topological properties. The proteins in tissue specific networks are interacting looser and the hubs play more important roles than those in the static network.     

Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell''s state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato''s Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.     

BMP signaling regulates the dorsal planarian midline and is needed for asymmetric regeneration

Planarians can be cut into irregularly shaped fragments capable of regenerating new and complete organisms. Such regenerative capacities involve a robust ability to restore bilateral symmetry. We have identified three genes needed for bilaterally asymmetric fragments to regenerate missing body parts. These genes are candidate components of a signaling pathway that controls the dorsal-ventral patterning of many animal embryos: a BMP1/Tolloid-like gene (smedolloid-1), a SMAD4-like gene (smedsmad4-1), and a BMP2/4/DPP-like gene (smedbmp4-1). BMP signaling was involved in the formation of new tissues at the midline of regeneration, the dorsal-ventral patterning of new tissues, and the maintenance of the dorsal-ventral pattern of existing adult tissue in homeostasis. smedbmp4-1 was normally expressed at the dorsal midline. Asymmetric fragments lacking a midline displayed new smedbmp4-1 expression prior to formation of a regenerative outgrowth (blastema). Asymmetric fragments containing the midline displayed expanded smedbmp4-1 expression towards the wound. We suggest injured animals that lack left-right symmetry reset their midline through modulation of BMP activity as an early and necessary event in regeneration.     

Expression profiling of autism candidate genes during human brain development implicates central immune signaling pathways

The Autism Spectrum Disorders (ASD) represent a clinically heterogeneous set of conditions with strong hereditary components. Despite substantial efforts to uncover the genetic basis of ASD, the genomic etiology appears complex and a clear understanding of the molecular mechanisms underlying Autism remains elusive. We hypothesized that focusing gene interaction networks on ASD-implicated genes that are highly expressed in the developing brain may reveal core mechanisms that are otherwise obscured by the genomic heterogeneity of the disorder. Here we report an in silico study of the gene expression profile from ASD-implicated genes in the unaffected developing human brain. By implementing a biologically relevant approach, we identified a subset of highly expressed ASD-candidate genes from which interactome networks were derived. Strikingly, immune signaling through NFκB, Tnf, and Jnk was central to ASD networks at multiple levels of our analysis, and cell-type specific expression suggested glia--in addition to neurons--deserve consideration. This work provides integrated genomic evidence that ASD-implicated genes may converge on central cytokine signaling pathways.     

Functions of beta2-adrenergic receptor in human periodontal ligament cells

Adrenergic receptors (ARs) are receptors of noradrenalin and adrenalin, of which there are nine different subtypes. In particular, β2 adrenergic receptor (β2-AR) is known to be related to the restoration and maintenance of homeostasis in bone and cardiac tissues; however, the functional role of signaling through β2-AR in periodontal ligament (PDL) tissue has not been fully examined. In this report, we investigated that β2-AR expression in PDL tissues and their features in PDL cells. β2-AR expressed in rat PDL tissues and human PDL cells (HPDLCs) derived from two different patients (HPDLCs-2G and -3S). Rat PDL tissue with occlusal loading showed high β2-AR expression, while its expression was downregulated in that without loading. In HPDLCs, β2-AR expression was increased exposed to stretch loading. The gene expression of PDL-related molecules was investigated in PDL clone cells (2-23 cells) overexpressing β2-AR. Their gene expression and intracellular cyclic adenosine monophosphate (cAMP) levels were also investigated in HPDLCs treated with a specific β2-AR agonist, fenoterol (FEN). Overexpression of β2-AR significantly promoted the gene expression of PDL-related molecules in 2 to 23 cells. FEN led to an upregulation in the expression of PDL-related molecules and increased intracellular cAMP levels in HPDLCs. In both HPDLCs, inhibition of cAMP signaling by using protein kinase A inhibitor suppressed the FEN-induced gene expression of α-smooth muscle actin. Our findings suggest that the occlusal force is important for β2-AR expression in PDL tissue and β2-AR is involved in fibroblastic differentiation and collagen synthesis of PDL cells. The signaling through β2-AR might be important for restoration and homeostasis of PDL tissue.