NetwoRx: connecting drugs to networks and phenotypes in Saccharomyces cerevisiae.

Drug modes of action are complex and still poorly understood. The set of known drug targets is widely acknowledged to be biased and incomplete, and so gives only limited insight into the system-wide effects of drugs. But a high-throughput assay unique to yeast-barcode-based chemogenomic screens-can measure the individual drug response of every yeast deletion mutant in parallel. NetwoRx (http://ophid.utoronto.ca/networx) is the first resource to store data from these extremely valuable yeast chemogenomics experiments. In total, NetwoRx stores data on 5924 genes and 466 drugs. In addition, we applied data-mining approaches to identify yeast pathways, functions and phenotypes that are targeted by particular drugs, compute measures of drug-drug similarity and construct drug-phenotype networks. These data are all available to search or download through NetwoRx; users can search by drug name, gene name or gene set identifier. We also set up automated analysis routines in NetwoRx; users can query new gene sets against the entire collection of drug profiles and retrieve the drugs that target them. We demonstrate with use case examples how NetwoRx can be applied to target specific phenotypes, repurpose drugs using mode of action analysis, investigate bipartite networks and predict new drugs that affect yeast aging.

NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs.

BACKGROUND: MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome--referred to as the micronome--to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal--mirDIP (http://ophid.utoronto.ca/mirDIP). RESULTS: mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs. CONCLUSIONS: Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.

Differential roles of cyclin D1 and D3 in pancreatic ductal adenocarcinoma.

BACKGROUND: The cyclin D1 (CCND1) and cyclin D3 (CCND3) are frequently co-overexpressed in pancreatic ductal adenocarcinoma (PDAC). Here we examine their differential roles in PDAC. RESULTS: CCND1 and CCND3 expression were selectively suppressed by shRNA in PDAC cell lines with expression levels of equal CCND1 and CCND3 (BxPC3), enhanced CCND1 (HPAC) or enhanced CCND3 (PANC1). Suppression of cell proliferation was greater with CCND3 than CCND1 downregulation. CCND3 suppression led to a reduced level of phosphorylated retinoblastoma protein (Ser795p-Rb/p110) and resulted in decreased levels of cyclin A mRNA and protein. A global gene expression analysis identified deregulated genes in D1- or D3-cyclin siRNA-treated PANC1 cells. The downregulated gene targets in CCND3 suppressed cells were significantly enriched in cell cycle associated processes (p < 0.005). In contrast, focal adhesion/actin cytoskeleton, MAPK and NF B signaling appeared to characterize the target genes and their interacting proteins in CCND1 suppressed PANC1 cells. CONCLUSIONS: Our results suggest that CCND3 is the primary driver of the cell cycle, in cooperation with CCND1 that integrates extracellular mitogenic signaling. We also present evidence that CCND1 plays a role in tumor cell migration. The results provide novel insights for common and differential targets of CCND1 and CCND3 overexpression during pancreatic duct cell carcinogenesis.

NAViGaTOR: Network Analysis, Visualization and Graphing Toronto.

SUMMARY: NAViGaTOR is a powerful graphing application for the 2D and 3D visualization of biological networks. NAViGaTOR includes a rich suite of visual mark-up tools for manual and automated annotation, fast and scalable layout algorithms and OpenGL hardware acceleration to facilitate the visualization of large graphs. Publication-quality images can be rendered through SVG graphics export. NAViGaTOR supports community-developed data formats (PSI-XML, BioPax and GML), is platform-independent and is extensible through a plug-in architecture. AVAILABILITY: NAViGaTOR is freely available to the research community from http://ophid.utoronto.ca/navigator/. Installers and documentation are provided for 32- and 64-bit Windows, Mac, Linux and Unix. CONTACT: juris@ai.utoronto.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.