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

Network-Based Pipeline for Analyzing MS Data: An Application toward Liver Cancer

10.1021/pr1010845Journal of Proteome Research1052261-2272JPRO

A potent betulinic acid analogue ascertains an antagonistic mechanism between autophagy and proteasomal degradation pathway in HT-29 cells

Betulinic acid (BA), a member of pentacyclic triterpenes has shown important biological activities like anti-bacterial, anti-malarial, anti-inflammatory and most interestingly anticancer property. To overcome its poor aqueous solubility and low bioavailability, structural modifications of its functional groups are made to generate novel lead(s) having better efficacy and less toxicity than the parent compound. BA analogue, 2c was found most potent inhibitor of colon cancer cell line, HT-29 cells with IC50 value 14.9 μM which is significantly lower than standard drug 5-fluorouracil as well as parent compound, Betulinic acid. We have studied another mode of PCD, autophagy which is one of the important constituent of cellular catabolic system as well as we also studied proteasomal degradation pathway to investigate whole catabolic pathway after exploration of 2c on HT-29 cells. Mechanism of autophagic cell death was studied using fluorescent dye like acridine orange (AO) and monodansylcadaverin (MDC) staining by using fluorescence microscopy. Various autophagic protein expression levels were determined by Western Blotting, qRT-PCR and Immunostaining. Confocal Laser Scanning Microscopy (CLSM) was used to study the colocalization of various autophagic proteins. These were accompanied by formation of autophagic vacuoles as revealed by FACS and transmission electron microscopy (TEM). Proteasomal degradation pathway was studied by proteasome-Glo™ assay systems using luminometer.The formation of autophagic vacuoles in HT-29 cells after 2c treatment was determined by fluorescence staining – confirming the occurrence of autophagy. In addition, 2c was found to alter expression levels of different autophagic proteins like Beclin-1, Atg 5, Atg 7, Atg 5-Atg 12, LC3B and autophagic adapter protein, p62. Furthermore we found the formation of autophagolysosome by colocalization of LAMP-1 with LC3B, LC3B with Lysosome, p62 with lysosome. Finally, as proteasomal degradation pathway downregulated after 2c treatment colocalization of ubiquitin with lysosome and LC3B with p62 was studied to confirm that protein degradation in autophagy induced HT-29 cells follows autolysosomal pathway. In summary, betulinic acid analogue, 2c was able to induce autophagy in HT-29 cells and as proteasomal degradation pathway downregulated after 2c treatment so protein degradation in autophagy induced HT-29 cell

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs

Looking at Cerebellar Malformations through Text-Mined Interactomes of Mice and Humans

WE HAVE GENERATED AND MADE PUBLICLY AVAILABLE TWO VERY LARGE NETWORKS OF MOLECULAR INTERACTIONS: 49,493 mouse-specific and 52,518 human-specific interactions. These networks were generated through automated analysis of 368,331 full-text research articles and 8,039,972 article abstracts from the PubMed database, using the GeneWays system. Our networks cover a wide spectrum of molecular interactions, such as bind, phosphorylate, glycosylate, and activate; 207 of these interaction types occur more than 1,000 times in our unfiltered, multi-species data set. Because mouse and human genes are linked through an orthological relationship, human and mouse networks are amenable to straightforward, joint computational analysis. Using our newly generated networks and known associations between mouse genes and cerebellar malformation phenotypes, we predicted a number of new associations between genes and five cerebellar phenotypes (small cerebellum, absent cerebellum, cerebellar degeneration, abnormal foliation, and abnormal vermis). Using a battery of statistical tests, we showed that genes that are associated with cerebellar phenotypes tend to form compact network clusters. Further, we observed that cerebellar malformation phenotypes tend to be associated with highly connected genes. This tendency was stronger for developmental phenotypes and weaker for cerebellar degeneration

Membrane vesicles, current state-of-the-art: emerging role of extracellular vesicles

Release of membrane vesicles, a process conserved in both prokaryotes and eukaryotes, represents an evolutionary link, and suggests essential functions of a dynamic extracellular vesicular compartment (including exosomes, microparticles or microvesicles and apoptotic bodies). Compelling evidence supports the significance of this compartment in a broad range of physiological and pathological processes. However, classification of membrane vesicles, protocols of their isolation and detection, molecular details of vesicular release, clearance and biological functions are still under intense investigation. Here, we give a comprehensive overview of extracellular vesicles. After discussing the technical pitfalls and potential artifacts of the rapidly emerging field, we compare results from meta-analyses of published proteomic studies on membrane vesicles. We also summarize clinical implications of membrane vesicles. Lessons from this compartment challenge current paradigms concerning the mechanisms of intercellular communication and immune regulation. Furthermore, its clinical implementation may open new perspectives in translational medicine both in diagnostics and therapy

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN

Hypoxic enhancement of exosome release by breast cancer cells

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citedBackground Exosomes are nanovesicles secreted by tumour cells which have roles in paracrine signalling during tumour progression, including tumour-stromal interactions, activation of proliferative pathways and bestowing immunosuppression. Hypoxia is an important feature of solid tumours which promotes tumour progression, angiogenesis and metastasis, potentially through exosome-mediated signalling. Methods Breast cancer cell lines were cultured under either moderate (1% O2) or severe (0.1% O2) hypoxia. Exosomes were isolated from conditioned media and quantitated by nanoparticle tracking analysis (NTA) and immunoblotting for the exosomal protein CD63 in order to assess the impact of hypoxia on exosome release. Hypoxic exosome fractions were assayed for miR-210 by real-time reverse transcription polymerase chain reaction and normalised to exogenous and endogenous control genes. Statistical significance was determined using the Student T test with a P value of < 0.05 considered significant. Results Exposure of three different breast cancer cell lines to moderate (1% O2) and severe (0.1% O2) hypoxia resulted in significant increases in the number of exosomes present in the conditioned media as determined by NTA and CD63 immunoblotting. Activation of hypoxic signalling by dimethyloxalylglycine, a hypoxia-inducible factor (HIF) hydroxylase inhibitor, resulted in significant increase in exosome release. Transfection of cells with HIF-1α siRNA prior to hypoxic exposure prevented the enhancement of exosome release by hypoxia. The hypoxically regulated miR-210 was identified to be present at elevated levels in hypoxic exosome fractions. Conclusions These data provide evidence that hypoxia promotes the release of exosomes by breast cancer cells, and that this hypoxic response may be mediated by HIF-1α. Given an emerging role for tumour cell-derived exosomes in tumour progression, this has significant implications for understanding the hypoxic tumour phenotype, whereby hypoxic cancer cells may release more exosomes into their microenvironment to promote their own survival and invasion.HK was recipient of a Flinders University Unibooks Honours Scholarship and the work was funded by the Flinders Medical Centre Research Foundation, the Lyn Wrigley Breast Cancer Research and Development Fund, and the Flinders Medical Centre Clinicians Special Purpose Fund