Kinin B2 receptor regulates chemokines CCL2 and CCL5 expression and modulates leukocyte recruitment and pathology in experimental autoimmune encephalomyelitis (EAE) in mice

<p>Abstract</p> <p>Background</p> <p>Kinins are important mediators of inflammation and act through stimulation of two receptor subtypes, B₁and B₂. Leukocyte infiltration contributes to the pathogenesis of autoimmune inflammation in the central nervous system (CNS), occurring not only in multiple sclerosis (MS) but also in experimental autoimmune encephalomyelitis (EAE). We have previously shown that the chemokines CCL2 and CCL5 play an important role in the adhesion of leukocytes to the brain microcirculation in EAE. The aim of the present study was to evaluate the relevance of B₂receptors to leukocyte-endothelium interactions in the cerebral microcirculation, and its participation in CNS inflammation in the experimental model of myelin-oligodendrocyte-glycoprotein (MOG)_35–55-induced EAE in mice.</p> <p>Methods</p> <p>In order to evaluate the role of B2 receptor in the cerebral microvasculature we used wild-type (WT) and kinin B2 receptor knockout (B₂^-/-) mice subjected to MOG_35–55-induced EAE. Intravital microscopy was used to investigate leukocyte recruitment on pial matter vessels in B₂^-/-and WT EAE mice. Histological documentation of inflammatory infiltrates in brain and spinal cords was correlated with intravital findings. The expression of CCL5 and CCL2 in cerebral tissue was assessed by ELISA.</p> <p>Results</p> <p>Clinical parameters of disease were reduced in B₂^-/-mice in comparison to wild type EAE mice. At day 14 after EAE induction, there was a significant decrease in the number of adherent leukocytes, a reduction of cerebral CCL5 and CCL2 expressions, and smaller inflammatory and degenerative changes in B₂^-/-mice when compared to WT.</p> <p>Conclusion</p> <p>Our results suggest that B₂receptors have two major effects in the control of EAE severity: (i) B₂regulates the expression of chemokines, including CCL2 and CCL5, and (ii) B₂modulates leukocyte recruitment and inflammatory lesions in the CNS.</p

MINE: Module Identification in Networks

<p>Abstract</p> <p>Background</p> <p>Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks.</p> <p>Results</p> <p>MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the <it>C. elegans </it>protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties.</p> <p>Conclusions</p> <p>MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both <it>S. cerevisiae </it>and <it>C. elegans</it>.</p

Bves Modulates Tight Junction Associated Signaling

Blood vessel epicardial substance (Bves) is a transmembrane adhesion protein that regulates tight junction (TJ) formation in a variety of epithelia. The role of TJs within epithelium extends beyond the mechanical properties. They have been shown to play a direct role in regulation of RhoA and ZONAB/DbpA, a y-box transcription factor. We hypothesize that Bves can modulate RhoA activation and ZONAB/DbpA activity through its regulatory effect on TJ formation. Immortalized human corneal epithelial (HCE) cells were stably transfected with Flag-tagged full length chicken Bves (w-Bves) or C-terminus truncated Bves (t-Bves). We found that stably transfected w-Bves and t-Bves were interacting with endogenous human Bves. However, interaction with t-Bves appeared to disrupt cell membrane localization of endogenous Bves and interaction with ZO-1. w-Bves cells exhibited increased TJ function reflected by increased trans-epithelial electrical resistance, while t-Bves cells lost TJ protein immunolocalization at cell-cell contacts and exhibited decreased trans-epithelial electrical resistance. In parental HCE and w-Bves cells ZONAB/DbpA and GEF-H1 were seen at cell borders in the same pattern as ZO-1. However, expression of t-Bves led to decreased membrane localization of both ZONAB/DbpA and GEF-H1. t-Bves cells had increased RhoA activity, as indicated by a significant 30% increase in FRET activity compared to parental HCE cells. ZONAB/DbpA transcriptional activity, assessed using a luciferase reporter probe, was increased in t-Bves cells. These studies demonstrate that Bves expression and localization can regulate RhoA and ZONAB/DbpA activity

Markov clustering versus affinity propagation for the partitioning of protein interaction graphs

<p>Abstract</p> <p>Background</p> <p>Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs.</p> <p>Results</p> <p>In this work we compare the performance of the Affinity Propagation (AP) and Markov Clustering (MCL) procedures. To this end we derive an unweighted network of protein-protein interactions from a set of 408 protein complexes from <it>S. cervisiae </it>hand curated in-house, and evaluate the performance of the two clustering algorithms in recalling the annotated complexes. In doing so the parameter space of each algorithm is sampled in order to select optimal values for these parameters, and the robustness of the algorithms is assessed by quantifying the level of complex recall as interactions are randomly added or removed to the network to simulate noise. To evaluate the performance on a weighted protein interaction graph, we also apply the two algorithms to the consolidated protein interaction network of <it>S. cerevisiae</it>, derived from genome scale purification experiments and to versions of this network in which varying proportions of the links have been randomly shuffled.</p> <p>Conclusion</p> <p>Our analysis shows that the MCL procedure is significantly more tolerant to noise and behaves more robustly than the AP algorithm. The advantage of MCL over AP is dramatic for unweighted protein interaction graphs, as AP displays severe convergence problems on the majority of the unweighted graph versions that we tested, whereas MCL continues to identify meaningful clusters, albeit fewer of them, as the level of noise in the graph increases. MCL thus remains the method of choice for identifying protein complexes from binary interaction networks.</p

Diffusion Model Based Spectral Clustering for Protein-Protein Interaction Networks

BACKGROUND: A goal of systems biology is to analyze large-scale molecular networks including gene expressions and protein-protein interactions, revealing the relationships between network structures and their biological functions. Dividing a protein-protein interaction (PPI) network into naturally grouped parts is an essential way to investigate the relationship between topology of networks and their functions. However, clear modular decomposition is often hard due to the heterogeneous or scale-free properties of PPI networks. METHODOLOGY/PRINCIPAL FINDINGS: To address this problem, we propose a diffusion model-based spectral clustering algorithm, which analytically solves the cluster structure of PPI networks as a problem of random walks in the diffusion process in them. To cope with the heterogeneity of the networks, the power factor is introduced to adjust the diffusion matrix by weighting the transition (adjacency) matrix according to a node degree matrix. This algorithm is named adjustable diffusion matrix-based spectral clustering (ADMSC). To demonstrate the feasibility of ADMSC, we apply it to decomposition of a yeast PPI network, identifying biologically significant clusters with approximately equal size. Compared with other established algorithms, ADMSC facilitates clear and fast decomposition of PPI networks. CONCLUSIONS/SIGNIFICANCE: ADMSC is proposed by introducing the power factor that adjusts the diffusion matrix to the heterogeneity of the PPI networks. ADMSC effectively partitions PPI networks into biologically significant clusters with almost equal sizes, while being very fast, robust and appealing simple

Heuristics for the inversion median problem

Background: The study of genome rearrangements has become a mainstay of phylogenetics and comparative genomics. Fundamental in such a study is the median problem: given three gene arrangements, find a fourth that minimizes the sum of the evolutionary distances between itself and the given three. Many exact algorithms and heuristics have been developped for the inversion median problem, of which the best known is MGR. Results: We present a unifying framework for median heuristics, which enables us to clarify existing strategies and to place them in a partial ordering. Analysis of this framework leads to a new insight: the best strategies continue to refer to the input data rather than just to updated estimates. Using this insight, we develop a new heuristic for inversion medians that uses input data to the end of its computation and leverages our previous work with DCJ medians. Finally, we present the results of extensive experimentation showing that our new heuristic outperforms all others in accuracy and, especially, in running time: the heuristic typically returns solutions within 1 % of optimal and runs in seconds to minutes even on genomes with 25’000 genes—in contrast, MGR can take days on instances of 200 genes and cannot be used beyond 1’000 genes. Conclusions: Finding good rearrangement medians, in particular inversion medians, had long been regarded as the computational bottleneck in whole-genome studies. Our new heuristic for inversion medians, ASM, which dominates all others in our framework, puts that issue to rest by providing near-optimal solutions within seconds to minutes on even the largest genomes

Visualizing the Needle in the Haystack: In Situ Hybridization With Fluorescent Dendrimers

In situ hybridization with 3DNA™ dendrimers is a novel tool for detecting low levels of mRNA in tissue sections and whole embryos. Fluorescently labeled dendrimers were used to identify cells that express mRNA for the skeletal muscle transcription factor MyoD in the early chick embryo. A small population of MyoD mRNA positive cells was found in the epiblast prior to the initiation of gastrulation, two days earlier than previously detected using enzymatic or radiolabeled probes for mRNA. When isolated from the epiblast and placed in culture, the MyoD mRNA positive cells were able to differentiate into skeletal muscle cells. These results demonstrate that DNA dendrimers are sensitive and precise tools for identifying low levels of mRNA in single cells and tissues

Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell

15 pages, 6 figures.-- 19604360 [PubMed]BACKGROUND: The characterization of the global functional structure of a cell is a major goal in bioinformatics and systems biology. Gene Ontology (GO) and the protein-protein interaction network offer alternative views of that structure. RESULTS: This study presents a comparison of the global structures of the Gene Ontology and the interactome of Saccharomyces cerevisiae. Sensitive, unsupervised methods of clustering applied to a large fraction of the proteome led to establish a GO-interactome correlation value of +0.47 for a general dataset that contains both high and low-confidence interactions and +0.58 for a smaller, high-confidence dataset. CONCLUSION: The structures of the yeast cell deduced from GO and interactome are substantially congruent. However, some significant differences were also detected, which may contribute to a better understanding of cell function and also to a refinement of the current ontologiesResearch supported by grant BIO2008-05067 (Programa Nacional de Biotecnología; Ministerio de Ciencia e Innovación. Spain), awarded to IM. AM was a FPI fellow from Ministerio de Educación y Ciencia (Spain).Peer reviewe

Effect of Crystallographic Texture on Magnetic Characteristics of Cobalt Nanowires

Cobalt nanowires with controlled diameters have been synthesized using electrochemical deposition in etched ion-track polycarbonate membranes. Structural characterization of these nanowires with diameter 70, 90, 120 nm and length 30 μm was performed by scanning electron microscopy, high-resolution transmission electron microscopy, and X-ray diffraction techniques. The as-prepared wires show uniform diameter along the whole length and X-ray diffraction analysis reveals that [002] texture of these wires become more pronounced as diameter is reduced. Magnetic characterization of the nanowires shows a clear difference of squareness and coercivity between parallel and perpendicular orientations of the wires with respect to the applied field direction. In case of parallel applied field, the coercivity has been found to be decreasing with increasing diameter of the wires while in perpendicular case; the coercivity observes lower values for larger diameter. The results are explained by taking into account the magnetocrystalline and shape anisotropies with respect to the applied field and domain transformation mechanism when single domain limit is surpassed