WikiPathways: building research communities on biological pathways.

Here, we describe the development of WikiPathways (http://www.wikipathways.org), a public wiki for pathway curation, since it was first published in 2008. New features are discussed, as well as developments in the community of contributors. New features include a zoomable pathway viewer, support for pathway ontology annotations, the ability to mark pathways as private for a limited time and the availability of stable hyperlinks to pathways and the elements therein. WikiPathways content is freely available in a variety of formats such as the BioPAX standard, and the content is increasingly adopted by external databases and tools, including Wikipedia. A recent development is the use of WikiPathways as a staging ground for centrally curated databases such as Reactome. WikiPathways is seeing steady growth in the number of users, page views and edits for each pathway. To assess whether the community curation experiment can be considered successful, here we analyze the relation between use and contribution, which gives results in line with other wiki projects. The novel use of pathway pages as supplementary material to publications, as well as the addition of tailored content for research domains, is expected to stimulate growth further

Gi-Coupled GPCR Signaling Controls the Formation and Organization of Human Pluripotent Colonies

BACKGROUND:Reprogramming adult human somatic cells to create human induced pluripotent stem (hiPS) cell colonies involves a dramatic morphological and organizational transition. These colonies are morphologically indistinguishable from those of pluripotent human embryonic stem (hES) cells. G protein-coupled receptors (GPCRs) are required in diverse developmental processes, but their role in pluripotent colony morphology and organization is unknown. We tested the hypothesis that G(i)-coupled GPCR signaling contributes to the characteristic morphology and organization of human pluripotent colonies. METHODOLOGY/PRINCIPAL FINDINGS:Specific and irreversible inhibition of G(i)-coupled GPCR signaling by pertussis toxin markedly altered pluripotent colony morphology. Wild-type hES and hiPS cells formed monolayer colonies, but colonies treated with pertussis toxin retracted inward, adopting a dense, multi-layered conformation. The treated colonies were unable to reform after a scratch wound insult, whereas control colonies healed completely within 48 h. In contrast, activation of an alternative GPCR pathway, G(s)-coupled signaling, with cholera toxin did not affect colony morphology or the healing response. Pertussis toxin did not alter the proliferation, apoptosis or pluripotency of pluripotent stem cells. CONCLUSIONS/SIGNIFICANCE:Experiments with pertussis toxin suggest that G(i) signaling plays a critical role in the morphology and organization of pluripotent colonies. These results may be explained by a G(i)-mediated density-sensing mechanism that propels the cells radially outward. GPCRs are a promising target for modulating the formation and organization of hiPS and hES cell colonies and may be important for understanding somatic cell reprogramming and for engineering pluripotent stem cells for therapeutic applications

BridgeDb: standardized access to gene, protein and metabolite identifier mapping services

Many interesting problems in bioinformatics require integration of data from various sources. For example when combining microarray data with a pathway database, or merging co-citation networks with protein-protein interaction networks. Invariably this leads to an identifier mapping problem, where different datasets are annotated with identifiers that are related, but originate from different databases.

Solutions for the identifier mapping problem exist, such as Biomart, Synergizer, Cronos, PICR, HMS and many more. This creates an opportunity for bioinformatics tool developers. Tools can be made to flexibly support multiple mapping services or mapping services could be combined to get broader coverage. This approach requires an interface layer between tools and mapping services. BridgeDb provides such an interface layer, in the form of both a Java and REST API.

Because of the standardized interface layer, BridgeDb is not tied to a specific source of mapping information. You can switch easily between flat files, relational databases and several different web services. Mapping services can be combined to support multi-omics experiments or to integrate custom microarray annotations. BridgeDb isn't just yet another mapping service: it tries to build further on existing work, and integrate multiple partial solutions. The framework is intended for customization and adaptation to any identifier mapping service. 

BridgeDb makes it easy to add an important capability to existing tools. BridgeDb has already been integrated into several popular bioinformatics applications, such as Cytoscape, WikiPathways, PathVisio, Vanted and Taverna. To encourage tool developers to start using BridgeDb, we've created code examples, online documentation, and a mailinglist to ask questions. 

We believe that, to meet the challenges that are encountered in bioinformatics today, the software development process should follow a few essential principles: user friendliness, code reuse, modularity and open source. BridgeDb adheres to these principles, and can serve as a useful model for others to follow. BridgeDb can function to increase user-friendliness of graphical applications. It re-uses work from other projects such as BioMart and MIRIAM. BridgeDb consists of several small modules, integrated through a common interface (API). Components of BridgeDb can be left out or replaced, for maximum flexibility. BridgeDb was open source from the very beginning of the project. The philosophy of open source is closely aligned to academic values, of building on top of the work of giants. 

Many interesting problems in bioinformatics require integration of data from various sources. For example when combining microarray data with a pathway database, or merging co-citation networks with protein-protein interaction networks. Invariably this leads to an identifier mapping problem, where different datasets are annotated with identifiers that are related, but originate from different databases.

Solutions for the identifier mapping problem exist, such as Biomart, Synergizer, Cronos, PICR, HMS and many more. This creates an opportunity for bioinformatics tool developers. Tools can be made to flexibly support multiple mapping services or mapping services could be combined to get broader coverage. This approach requires an interface layer between tools and mapping services. BridgeDb provides such an interface layer, in the form of both a Java and REST API.

Because of the standardized interface layer, BridgeDb is not tied to a specific source of mapping information. You can switch easily between flat files, relational databases and several different web services. Mapping services can be combined to support multi-omics experiments or to integrate custom microarray annotations. BridgeDb isn't just yet another mapping service: it tries to build further on existing work, and integrate multiple partial solutions. The framework is intended for customization and adaptation to any identifier mapping service. 

BridgeDb makes it easy to add an important capability to existing tools. BridgeDb has already been integrated into several popular bioinformatics applications, such as Cytoscape, WikiPathways, PathVisio, Vanted and Taverna. To encourage tool developers to start using BridgeDb, we've created code examples, online documentation, and a mailinglist to ask questions. 

We believe that, to meet the challenges that are encountered in bioinformatics today, the software development process should follow a few essential principles: user friendliness, code reuse, modularity and open source. BridgeDb adheres to these principles, and can serve as a useful model for others to follow. BridgeDb can function to increase user-friendliness of graphical applications. It re-uses work from other projects such as BioMart and MIRIAM. BridgeDb consists of several small modules, integrated through a common interface (API). Components of BridgeDb can be left out or replaced, for maximum flexibility. BridgeDb was open source from the very beginning of the project. The philosophy of open source is closely aligned to academic values, of building on top of the work of giants. 

The BridgeDb library is available at "http://www.bridgedb.org":http://www.bridgedb.org.
A paper about BridgeDb was published in BMC _Bioinformatics_, 2010 Jan 4;11(1):5.

BridgeDb blog: "http://www.helixsoft.nl/blog/?tag=bridgedb":http://www.helixsoft.nl/blog/?tag=bridged

Engineered G protein coupled receptors reveal independent regulation of internalization, desensitization and acute signaling

BACKGROUND: The physiological regulation of G protein-coupled receptors, through desensitization and internalization, modulates the length of the receptor signal and may influence the development of tolerance and dependence in response to chronic drug treatment. To explore the importance of receptor regulation, we engineered a series of G(i)-coupled receptors that differ in signal length, degree of agonist-induced internalization, and ability to induce adenylyl cyclase superactivation. All of these receptors, based on the kappa opioid receptor, were modified to be receptors activated solely by synthetic ligands (RASSLs). This modification allows us to compare receptors that have the same ligands and effectors, but differ only in desensitization and internalization. RESULTS: Removal of phosphorylation sites in the C-terminus of the RASSL resulted in a mutant that was resistant to internalization and less prone to desensitization. Replacement of the C-terminus of the RASSL with the corresponding portion of the mu opioid receptor eliminated the induction of AC superactivation, without disrupting agonist-induced desensitization or internalization. Surprisingly, removal of phosphorylation sites from this chimera resulted in a receptor that is constitutively internalized, even in the absence of agonist. However, the receptor still signals and desensitizes in response to agonist, indicating normal G-protein coupling and partial membrane expression. CONCLUSIONS: These studies reveal that internalization, desensitization and adenylyl cyclase superactivation, all processes that decrease chronic G(i)-receptor signals, are independently regulated. Furthermore, specific mutations can radically alter superactivation or internalization without affecting the efficacy of acute G(i )signaling. These mutant RASSLs will be useful for further elucidating the temporal dynamics of the signaling of G protein-coupled receptors in vitro and in vivo

Contractile deficits in engineered cardiac microtissues as a result of MYBPC3 deficiency and mechanical overload.

The integration of in vitro cardiac tissue models, human induced pluripotent stem cells (hiPSCs) and genome-editing tools allows for the enhanced interrogation of physiological phenotypes and recapitulation of disease pathologies. Here, using a cardiac tissue model consisting of filamentous three-dimensional matrices populated with cardiomyocytes derived from healthy wild-type (WT) hiPSCs (WT hiPSC-CMs) or isogenic hiPSCs deficient in the sarcomere protein cardiac myosin-binding protein C (MYBPC3-/- hiPSC-CMs), we show that the WT microtissues adapted to the mechanical environment with increased contraction force commensurate to matrix stiffness, whereas the MYBPC3-/- microtissues exhibited impaired force development kinetics regardless of matrix stiffness and deficient contraction force only when grown on matrices with high fibre stiffness. Under mechanical overload, the MYBPC3-/- microtissues had a higher degree of calcium transient abnormalities, and exhibited an accelerated decay of calcium dynamics as well as calcium desensitization, which accelerated when contracting against stiffer fibres. Our findings suggest that MYBPC3 deficiency and the presence of environmental stresses synergistically lead to contractile deficits in cardiac tissues

Regression Approaches for Microarray Data Analysis

A variety of new procedures have been devised to handle the two sample comparison (e.g., tumor versus normal tissue) of gene expression values as measured with microarrays. Such new methods are required in part because of some defining characteristics of microarray-based studies: (i) the very large number of genes contributing expression measures which far exceeds the number of samples (observations) available, and (ii) the fact that by virtue of pathway/network relationships, the gene expression measures tend to be highly correlated. These concerns are exacerbated in the regression setting, where the objective is to relate gene expression, simultaneously for multiple genes, to some external outcome or phenotype. Correspondingly, several methods have been recently proposed for addressing these issues. We briefly critique some of these methods prior to a detailed evaluation of gene harvesting. This reveals that gene harvesting, without additional constraints, can yield artifactual solutions. Results obtained employing such constraints motivate the use of regularized regression procedures such as the lasso, least angle regression, and support vector machines. Model selection and solution multiplicity issues are also discussed. The methods are evaluated using a microarraybased study of cardiomyopathy in transgenic mice

WikiPathways: Pathway Editing for the People

WikiPathways provides a collaborative platform for creating, updating, and sharing pathway diagrams and serves as an example of content curation by the biology community

MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data

MAPPFinder is a tool that creates a global gene-expression profile across all areas of biology by integrating the annotations of the Gene Ontology (GO) Project with the free software package GenMAPP . The results are displayed in a searchable browser, allowing the user to rapidly identify GO terms with over-represented numbers of gene-expression changes. Clicking on GO terms generates GenMAPP graphical files where gene relationships can be explored, annotated, and files can be freely exchanged

Identifying genetic networks underlying myometrial transition to labor

BACKGROUND: Early transition to labor remains a major cause of infant mortality, yet the causes are largely unknown. Although several marker genes have been identified, little is known about the underlying global gene expression patterns and pathways that orchestrate these striking changes. RESULTS: We performed a detailed time-course study of over 9,000 genes in mouse myometrium at defined physiological states: non-pregnant, mid-gestation, late gestation, and postpartum. This dataset allowed us to identify distinct patterns of gene expression that correspond to phases of myometrial 'quiescence', 'term activation', and 'postpartum involution'. Using recently developed functional mapping tools (HOPACH (hierarchical ordered partitioning and collapsing hybrid) and GenMAPP 2.0), we have identified new potential transcriptional regulatory gene networks mediating the transition from quiescence to term activation. CONCLUSIONS: These results implicate the myometrium as an essential regulator of endocrine hormone (cortisol and progesterone synthesis) and signaling pathways (cyclic AMP and cyclic GMP stimulation) that direct quiescence via the transcripitional upregulation of both novel and previously associated regulators. With term activation, we observe the upregulation of cytoskeletal remodeling mediators (intermediate filaments), cell junctions, transcriptional regulators, and the coordinate downregulation of negative control checkpoints of smooth muscle contractile signaling. This analysis provides new evidence of multiple parallel mechanisms of uterine contractile regulation and presents new putative targets for regulating myometrial transformation and contraction