1,335 research outputs found

    Building and analyzing protein interactome networks by cross-species comparisons

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    <p>Abstract</p> <p>Background</p> <p>A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast) and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species.</p> <p>Results</p> <p>The connectivity of the resultant networks was determined to have scale-free distribution, small-world properties, and increased local modularity, indicating that the added interactions do not disrupt our current understanding of protein network structures. We show examples of how these improved interactomes can be used to analyze a genome-scale dataset (RNAi screen) and to assign new function to proteins. Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced.</p> <p>Conclusions</p> <p>Protein-protein interactions were predicted in five species, based on orthology. An InteroScore, a score accounting for homology, number of orthologues with evidence of interactions, and number of unique observations of interactions, is given to each known and predicted interaction. Our website <url>http://www.interologfinder.org</url> provides research biologists intuitive access to this data.</p

    Protein interactions across and between eukaryotic kingdoms: networks, inference strategies, integration of functional data and evolutionary dynamics

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    Thesis (Ph.D.)--Boston UniversityHow cellular elements coordinate their function is a fundamental question in biology. A crucial step towards understanding cellular systems is the mapping of physical interactions between protein, DNA, RNA and other macromolecules or metabolites. Genome-scale technologies have yielded protein-protein interaction networks for several eukaryotic species and have provided insight into biological processes and evolution, but many of the currently available networks are biased. Towards a true human protein-protein interaction network, we examined literature-based aggregations of lowthroughput experiments, high-throughput experimental networks validated using different strategies, and predicted interaction networks to infer how the underlying interactome may differ from current maps. Using systematically mapped interactome networks, which appear to be the least biased, we explored the functional organization of Arabidopsis thaliana and characterize the asymmetric divergence of duplicated paralogous proteins through their interaction profiles. To further dissect the relationship between interactions and function enforced by evolution, we investigated a first-of-its-kind systematic crossspecies human-yeast hybrid interactome network. Although the cross-species network is topologically similar to conventional intra-species networks, we found signatures of dynamic changes in interaction propensities due to countervailing evolutionary forces. Collectively, these analyses of human, plant and yeast interactome networks bridge separate experiments to characterize bias, function and evolution across eukaryotic kingdoms

    Detecting similar binding pockets to enable systems polypharmacology

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    In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia

    Gene regulatory network reveals oxidative stress as the underlying molecular mechanism of type 2 diabetes and hypertension

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    <p>Abstract</p> <p>Background</p> <p>The prevalence of diabetes is increasing worldwide. It has been long known that increased rates of inflammatory diseases, such as obesity (OBS), hypertension (HT) and cardiovascular diseases (CVD) are highly associated with type 2 diabetes (T2D). T2D and/or OBS can develop independently, due to genetic, behavioral or lifestyle-related variables but both lead to oxidative stress generation. The underlying mechanisms by which theses complications arise and manifest together remain poorly understood. Protein-protein interactions regulate nearly every living process. Availability of high-throughput genomic data has enabled unprecedented views of gene and protein co-expression, co-regulations and interactions in cellular systems.</p> <p>Methods</p> <p>The present work, applied a systems biology approach to develop gene interaction network models, comprised of high throughput genomic and PPI data for T2D. The genes differentially regulated through T2D were 'mined' and their 'wirings' were studied to get a more complete understanding of the overall gene network topology and their role in disease progression.</p> <p>Results</p> <p>By analyzing the genes related to T2D, HT and OBS, a highly regulated gene-disease integrated network model has been developed that provides useful functional linkages among groups of genes and thus addressing how different inflammatory diseases are connected and propagated at genetic level. Based on the investigations around the 'hubs' that provided more meaningful insights about the cross-talk within gene-disease networks in terms of disease phenotype association with oxidative stress and inflammation, a hypothetical co-regulation disease mechanism model been proposed. The results from this study revealed that the oxidative stress mediated regulation cascade is the common mechanistic link among the pathogenesis of T2D, HT and other inflammatory diseases such as OBS.</p> <p>Conclusion</p> <p>The findings provide a novel comprehensive approach for understanding the pathogenesis of various co-associated chronic inflammatory diseases by combining the power of pathway analysis with gene regulatory network evaluation.</p

    Interactomics-Based Functional Analysis: Using Interaction Conservation To Probe Bacterial Protein Functions

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    The emergence of genomics as a discrete field of biology has changed humanity’s understanding of our relationship with bacteria. Sequencing the genome of each newly-discovered bacterial species can reveal novel gene sequences, though the genome may contain genes coding for hundreds or thousands of proteins of unknown function (PUFs). In some cases, these coding sequences appear to be conserved across nearly all bacteria. Exploring the functional roles of these cases ideally requires an integrative, cross-species approach involving not only gene sequences but knowledge of interactions among their products. Protein interactions, studied at genome scale, extend genomics into the field of interactomics. I have employed novel computational methods to provide context for bacterial PUFs and to leverage the rich genomic, proteomic, and interactomic data available for hundreds of bacterial species. The methods employed in this study began with sets of protein complexes. I initially hypothesized that, if protein interactions reveal protein functions and interactions are frequently conserved through protein complexes, then conserved protein functions should be revealed through the extent of conservation of protein complexes and their components. The subsequent analyses revealed how partial protein complex conservation may, unexpectedly, be the rule rather than the exception. Next, I expanded the analysis by combining sets of thousands of experimental protein-protein interactions. Progressing beyond the scope of protein complexes into interactions across full proteomes revealed novel evolutionary consistencies across bacteria but also exposed deficiencies among interactomics-based approaches. I have concluded this study with an expansion beyond bacterial protein interactions and into those involving bacteriophage-encoded proteins. This work concerns emergent evolutionary properties among bacterial proteins. It is primarily intended to serve as a resource for microbiologists but is relevant to any research into evolutionary biology. As microbiomes and their occupants become increasingly critical to human health, similar approaches may become increasingly necessary

    BioNetBuilder2.0: bringing systems biology to chicken and other model organisms

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    BACKGROUND:Systems Biology research tools, such as Cytoscape, have greatly extended the reach of genomic research. By providing platforms to integrate data with molecular interaction networks, researchers can more rapidly begin interpretation of large data sets collected for a system of interest. BioNetBuilder is an open-source client-server Cytoscape plugin that automatically integrates molecular interactions from all major public interaction databases and serves them directly to the user's Cytoscape environment. Until recently however, chicken and other eukaryotic model systems had little interaction data available.RESULTS:Version 2.0 of BioNetBuilder includes a redesigned synonyms resolution engine that enables transfer and integration of interactions across speciesthis engine translates between alternate gene names as well as between orthologs in multiple species. Additionally, BioNetBuilder is now implemented to be part of the Gaggle, thereby allowing seamless communication of interaction data to any software implementing the widely used Gaggle software. Using BioNetBuilder, we constructed a chicken interactome possessing 72,000 interactions among 8,140 genes directly in the Cytoscape environment. In this paper, we present a tutorial on how to do so and analysis of a specific use case.CONCLUSION:BioNetBuilder 2.0 provides numerous user-friendly systems biology tools that were otherwise inaccessible to researchers in chicken genomics, as well as other model systems. We provide a detailed tutorial spanning all required steps in the analysis. BioNetBuilder 2.0, the tools for maintaining its data bases, standard operating procedures for creating local copies of its back-end data bases, as well as all of the Gaggle and Cytoscape codes required, are open-source and freely available at http://err.bio.nyu.edu/cytoscape/bionetbuilder/ webcite.This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at [email protected]

    The Symbiosis Interactome: a computational approach reveals novel components, functional interactions and modules in Sinorhizobium meliloti

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    <p>Abstract</p> <p>Background</p> <p><it>Rhizobium</it>-Legume symbiosis is an attractive biological process that has been studied for decades because of its importance in agriculture. However, this system has undergone extensive study and although many of the major factors underpinning the process have been discovered using traditional methods, much remains to be discovered.</p> <p>Results</p> <p>Here we present an analysis of the 'Symbiosis Interactome' using novel computational methods in order to address the complex dynamic interactions between proteins involved in the symbiosis of the model bacteria <it>Sinorhizobium meliloti </it>with its plant hosts. Our study constitutes the first large-scale analysis attempting to reconstruct this complex biological process, and to identify novel proteins involved in establishing symbiosis. We identified 263 novel proteins potentially associated with the Symbiosis Interactome. The topology of the Symbiosis Interactome was used to guide experimental techniques attempting to validate novel proteins involved in different stages of symbiosis. The contribution of a set of novel proteins was tested analyzing the symbiotic properties of several <it>S. meliloti </it>mutants. We found mutants with altered symbiotic phenotypes suggesting novel proteins that provide key complementary roles for symbiosis.</p> <p>Conclusion</p> <p>Our 'systems-based model' represents a novel framework for studying host-microbe interactions, provides a theoretical basis for further experimental validations, and can also be applied to the study of other complex processes such as diseases.</p
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