A protein network-guided screen for cell cycle regulators in Drosophila

Background: Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both

DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions

<p>Abstract</p> <p>Background</p> <p>Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, <it>Drosophila melanogaster</it>.</p> <p>Description</p> <p>DroID, the <it>Drosophila </it>Interactions Database, is a comprehensive interactions database designed specifically for <it>Drosophila</it>. DroID houses published physical protein interactions, genetic interactions, and computationally predicted interactions, including interologs based on data for other model organisms and humans. All interactions are annotated with original experimental data and source information. DroID can be searched and filtered based on interaction information or a comprehensive set of gene attributes from Flybase. DroID also contains gene expression and expression correlation data that can be searched and used to filter datasets, for example, to focus a study on sub-networks of co-expressed genes. To address the inherent noise in interaction data, DroID employs an updatable confidence scoring system that assigns a score to each physical interaction based on the likelihood that it represents a biologically significant link.</p> <p>Conclusion</p> <p>DroID is the most comprehensive interactions database available for <it>Drosophila</it>. To facilitate downstream analyses, interactions are annotated with original experimental information, gene expression data, and confidence scores. All data in DroID are freely available and can be searched, explored, and downloaded through three different interfaces, including a text based web site, a Java applet with dynamic graphing capabilities (IM Browser), and a Cytoscape plug-in. DroID is available at <url>http://www.droidb.org</url>.</p

A proteome-wide protein interaction map for Campylobacter jejuni

'Systematic identification of protein interactions for the bacterium Campylobacter jejuni using high-throughput yeast two-hybrid screens detected interactions for 80% of the organism's proteins

Moving to capture children’s attention: developing a methodology for measuring visuomotor attention

Attention underpins many activities integral to a child’s development. However, methodological limitations currently make large-scale assessment of children’s attentional skill impractical, costly and lacking in ecological validity. Consequently we developed a measure of ‘Visual Motor Attention’ (VMA) - a construct defined as the ability to sustain and adapt visuomotor behaviour in response to task-relevant visual information. In a series of experiments, we evaluated the capability of our method to measure attentional processes and their contributions in guiding visuomotor behaviour. Experiment 1 established the method’s core features (ability to track stimuli moving on a tablet-computer screen with a hand-held stylus) and demonstrated its sensitivity to principled manipulations in adults’ attentional load. Experiment 2 standardised a format suitable for use with children and showed construct validity by capturing developmental changes in executive attention processes. Experiment 3 tested the hypothesis that children with and without coordination difficulties would show qualitatively different response patterns, finding an interaction between the cognitive and motor factors underpinning responses. Experiment 4 identified associations between VMA performance and existing standardised attention assessments and thereby confirmed convergent validity. These results establish a novel approach to measuring childhood attention that can produce meaningful functional assessments that capture how attention operates in an ecologically valid context (i.e. attention's specific contribution to visuomanual action)

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

From protein networks to biological systems

Abstract A system-level understanding of any biological process requires a map of the relationships among the various molecules involved. Technologies to detect and predict protein interactions have begun to produce very large maps of protein interactions, some including most of an organismÕs proteins. These maps can be used to study how proteins work together to form molecular machines and regulatory pathways. They also provide a framework for constructing predictive models of how information and energy flow through biological networks. In many respects, protein interaction maps are an entrée into systems biology

Identification of New Protein Interactions between Dengue Fever Virus and Its Hosts, Human and Mosquito

<div><p>The four divergent serotypes of dengue virus are the causative agents of dengue fever, dengue hemorrhagic fever and dengue shock syndrome. About two-fifths of the world's population live in areas where dengue is prevalent, and thousands of deaths are caused by the viruses every year. Dengue virus is transmitted from one person to another primarily by the yellow fever mosquito, <em>Aedes aegypti</em>. Recent studies have begun to define how the dengue viral proteins interact with host proteins to mediate viral replication and pathogenesis. A combined analysis of these studies, however, suggests that many virus-host protein interactions remain to be identified, especially for the mosquito host. In this study, we used high-throughput yeast two-hybrid screening to identify mosquito and human proteins that physically interact with dengue proteins. We tested each identified host protein against the proteins from all four serotypes of dengue to identify interactions that are conserved across serotypes. We further confirmed many of the interactions using co-affinity purification assays. As in other large-scale screens, we identified some previously detected interactions and many new ones, moving us closer to a complete host – dengue protein interactome. To help summarize and prioritize the data for further study, we combined our interactions with other published data and identified a subset of the host-dengue interactions that are now supported by multiple forms of evidence. These data should be useful for understanding the interplay between dengue and its hosts and may provide candidates for drug targets and vector control strategies.</p> </div

Pie charts showing the number of host proteins that interacted with the corresponding proteins from one, two, three, or all four dengue serotypes.

<p>(A) Mosquito proteins. (B) Human proteins.</p

Dengue-host interactions supported by multiple forms of evidence.

<p>(A) Dengue-human interactome. (B) Dengue-mosquito interactome. Pink nodes represent host proteins. Green nodes represent dengue proteins. Red edges represent PPI with conserved interologs. Additional details are available in Cytoscape files in supplemental data (Data S1).</p

Dengue – host protein networks derived from two-hybrid screens and co-AP assays in this study.

<p>(A) Human-dengue interaction map. (B) Mosquito-dengue interaction map. Edges represent protein-protein interactions. Green nodes are dengue proteins, yellow nodes are host proteins, and blue nodes are host proteins found in both the human and mosquito maps. Red edges represent protein-protein interactions universally detected for all four serotypes. Blue edges represent protein-protein interactions confirmed by co-AP assays. Green edges represent the universal interactions that were confirmed by co-AP assays. Additional details are available in Cytoscape files in supplemental data (Data S1).</p