Genomic analysis of Xenopus organizer function

BACKGROUND: Studies of the Xenopus organizer have laid the foundation for our understanding of the conserved signaling pathways that pattern vertebrate embryos during gastrulation. The two primary activities of the organizer, BMP and Wnt inhibition, can regulate a spectrum of genes that pattern essentially all aspects of the embryo during gastrulation. As our knowledge of organizer signaling grows, it is imperative that we begin knitting together our gene-level knowledge into genome-level signaling models. The goal of this paper was to identify complete lists of genes regulated by different aspects of organizer signaling, thereby providing a deeper understanding of the genomic mechanisms that underlie these complex and fundamental signaling events. RESULTS: To this end, we ectopically overexpress Noggin and Dkk-1, inhibitors of the BMP and Wnt pathways, respectively, within ventral tissues. After isolating embryonic ventral halves at early and late gastrulation, we analyze the transcriptional response to these molecules within the generated ectopic organizers using oligonucleotide microarrays. An efficient statistical analysis scheme, combined with a new Gene Ontology biological process annotation of the Xenopus genome, allows reliable and faithful clustering of molecules based upon their roles during gastrulation. From this data, we identify new organizer-related expression patterns for 19 genes. Moreover, our data sub-divides organizer genes into separate head and trunk organizing groups, which each show distinct responses to Noggin and Dkk-1 activity during gastrulation. CONCLUSION: Our data provides a genomic view of the cohorts of genes that respond to Noggin and Dkk-1 activity, allowing us to separate the role of each in organizer function. These patterns demonstrate a model where BMP inhibition plays a largely inductive role during early developmental stages, thereby initiating the suites of genes needed to pattern dorsal tissues. Meanwhile, Wnt inhibition acts later during gastrulation, and is essential for maintenance of organizer gene expression throughout gastrulation, a role which may depend on its ability to block the expression of a host of ventral, posterior, and lateral fate-specifying factors

INTELLIGENT CLUSTER PROVISIONING

Currently, cluster formation through a network management system (NMS) within a software-defined wide area network (SD-WAN) is a manual, multi-step process that is error-prone and time-consuming and which requires multiple interventions (along with validations) by a user at each and every intermediate step. Moreover, that process is extremely difficult to scale with the increased cluster sizes that are required in larger deployments. To address the types of challenges that were described above, techniques are presented herein that support an end-to-end automation of the above-described cluster provisioning process. Aspects of the presented techniques encompass the scanning of an inter-cluster network and the automatic discovery of peer nodes that are viable for cluster formation; the automatic checking of prerequisites for cluster formation (which may include checks regarding a central processing unit (CPU), memory, disk resources, a persona, and a current software version); and support for a ‘one touch’ provisioning tool that may be employed by a network equipment vendor’s support staff or a customer to manage, expand, discover, form, etc. a cluster

INTELLIGENT CLUSTER PROVISIONING

Currently, cluster formation through a network management system (NMS) within a software-defined wide area network (SD-WAN) is a manual, multi-step process that is error-prone and time-consuming and which requires multiple interventions (along with validations) by a user at each and every intermediate step. Moreover, that process is extremely difficult to scale with the increased cluster sizes that are required in larger deployments. To address the types of challenges that were described above, techniques are presented herein that support an end-to-end automation of the above-described cluster provisioning process. Aspects of the presented techniques encompass the scanning of an inter-cluster network and the automatic discovery of peer nodes that are viable for cluster formation; the automatic checking of prerequisites for cluster formation (which may include checks regarding a central processing unit (CPU), memory, disk resources, a persona, and a current software version); and support for a ‘one touch’ provisioning tool that may be employed by a network equipment vendor’s support staff or a customer to manage, expand, discover, form, etc. a cluster

An integrative approach to ortholog prediction for disease-focused and other functional studies

Background Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. Results We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt webcite), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist webcite). Conclusions DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.Harvard CatalystNational Institutes of Health (U.S.) (NIH R01 GM067761)National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (5K08DK78361)Dana-Farber/Harvard Cancer Cente

GOPET: A tool for automated predictions of Gene Ontology terms

BACKGROUND: Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions. DESCRIPTION: We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO). Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool). It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via CONCLUSION: Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user

Integrating protein-protein interaction networks with phenotypes reveals signs of interactions

A major objective of systems biology is to organize molecular interactions as networks and to characterize information-flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the “signs” of interactions (i.e. activation/inhibition relationships). We constructed a Drosophila melanogaster signed PPI network, consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes Enolase and Aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation/inhibition relationships between physically interacting proteins within signaling pathways will impact our understanding of many biological functions, including signal transduction and mechanisms of disease

HIPPIE: Integrating Protein Interaction Networks with Experiment Based Quality Scores

Protein function is often modulated by protein-protein interactions (PPIs) and therefore defining the partners of a protein helps to understand its activity. PPIs can be detected through different experimental approaches and are collected in several expert curated databases. These databases are used by researchers interested in examining detailed information on particular proteins. In many analyses the reliability of the characterization of the interactions becomes important and it might be necessary to select sets of PPIs of different confidence levels. To this goal, we generated HIPPIE (Human Integrated Protein-Protein Interaction rEference), a human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIE's scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool (available at http://cbdm.mdc-berlin.de/tools/hippie) allows researchers to do network analyses focused on likely true PPI sets by generating subnetworks around proteins of interest at a specified confidence level

Genetic Determinants of Phosphate Response in Drosophila

Phosphate is required for many important cellular processes and having too little phosphate or too much can cause disease and reduce life span in humans. However, the mechanisms underlying homeostatic control of extracellular phosphate levels and cellular effects of phosphate are poorly understood. Here, we establish Drosophila melanogaster as a model system for the study of phosphate effects. We found that Drosophila larval development depends on the availability of phosphate in the medium. Conversely, life span is reduced when adult flies are cultured on high phosphate medium or when hemolymph phosphate is increased in flies with impaired Malpighian tubules. In addition, RNAi-mediated inhibition of MAPK-signaling by knockdown of Ras85D, phl/D-Raf or Dsor1/MEK affects larval development, adult life span and hemolymph phosphate, suggesting that some in vivo effects involve activation of this signaling pathway by phosphate. To identify novel genetic determinants of phosphate responses, we used Drosophila hemocyte-like cultured cells (S2R+) to perform a genome-wide RNAi screen using MAPK activation as the readout. We identified a number of candidate genes potentially important for the cellular response to phosphate. Evaluation of 51 genes in live flies revealed some that affect larval development, adult life span and hemolymph phosphate levels