21 research outputs found

    Network properties of interacting proteins.

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    <p>Cumulative log-log plots of (A) node centralities and (B) degrees for six subsets of nodes in the whole human protein-protein interaction network: the red curve is for the set of proteins in the human PPI network that do not interact with any pathogen in our dataset; the green line is for the set interacting with <i>B. anthracis</i>; the dark blue line is the for set interacting with <i>F. tularensis</i>; the purple line is for the set interacting with <i>Y. pestis</i>; the light blue line is for the set interacting with at least two pathogens; and the orange line is for the set interacting with all three pathogens. The fraction of proteins at a particular value of degree or centrality is the number of proteins having that value or greater divided by the number of proteins in the set. (Counts in parentheses represent the number of proteins in each set.)</p

    Summary of human-pathogen interactions.

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    <p>Counts in columns marked with an “*” correspond to pathogen proteins labeled as “putative”, “uncharacterized”, or “hypothetical”.</p

    Inparanoid ortholog groups.

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    <p>Summary of ortholog groups identified by Inparanoid <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012089#pone.0012089-Remm1" target="_blank">[28]</a>. The column marked “# clusters (>2 proteins)” is the number of orthologous clusters that contain more than a single protein from each organism. The column marked “# clusters (pathogen interactors)” is the number of orthologous clusters which contain a pathogen protein from each organism that is known to interact with a human protein in our dataset.</p

    Overview of experimental workflow.

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    <p>A) Overview of analysis pipeline used in this study. B) Venn diagram displaying the number of human proteins interacting with each of the three pathogens in this study.</p

    Interactions with host innate immune response.

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    <p>Interactions of human proteins involved in the innate immune response. We divided the human protein into subsets based on whether they induce or prevent apoptosis, or whether they regulate apoptosis. Proteins in the group labeled “Non-specific” do not have an annotation more specific than “Apoptosis” in the Gene Ontology <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012089#pone.0012089-Ashburner1" target="_blank">[20]</a>. For clarity this image shows only interactions involving virulence factors and uncharacterized pathogen proteins. As a result, some human proteins in the figure may appear to have no interacting partners.</p

    Conserved interactions.

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    <p>Summary of bacterial interologs. each row is a pair of bacteria, column 1 is the number orthologous pairs of proteins that both interact with a human protein, column 2 is number of these pairs that interact with the same protein, column 3 is number of these pairs that interact with human proteins that interact themselves, column 4 is number of these pairs that interact with paralogous human proteins.</p

    GSEA results.

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    <p>Summary of GSEA results for protein degree and betweenness centrality of human proteins for three networks: (W) whole human PPI network, (HT) the human PPI network generated by only considering high-throughput experiments, and (C) the human PPI network generated by only considering manually curated PPIs. The “# proteins in group” displays the total number of human proteins with at least one interaction. The “ES” columns display the enrichment score calculated by the GSEA for degree and for centrality. The column titled “# proteins contributing” displays the number of proteins contributing to the ES score.</p

    CPIM results.

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    <p>Summary of the number of identified CPIMs for each of the algorithms used in this study.</p

    Effect of <i>Helicobacter pylori</i> infection on plasma glucose concentrations, obtained from a glucose tolerance test (GTT).

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    <p>Mice were administered an intraperitoneal glucose challenge (2 g/kg body weight). <b>Panel A</b>: Glucose levels in leptin receptor-deficient (db/db) mice infected with either the wild-type <i>H. pylori</i> 98–325 (solid line), the isogenic <i>H. pylori</i> 99–305 (dotted line), or uninfected (control) (dashed line). Blood was collected before (0), then 15, 60, and 90 minutes after glucose load, (n = 10 mice/group). <b>Panel B</b>: Mouse model of diet-induced obesity; DIO mice infected with <i>H. pylori</i> 99–305 (dotted line), or uninfected (dashed line). <b>Panels C & D</b> illustrate the area under the curve calculations for the glucose concentrations during a GTT in the db/db and DIO models, as in Panels A & B respectively. Blood was collected before (0), then 15, 45, and 90 minutes of glucose load, (n = 10 mice/group). Statistically significant differences (<i>P</i><0.05) between treatments (*) are indicated.</p

    Effect of <i>Helicobacter pylori</i> infection on infiltration of immune cell subsets into adipose tissue.

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    <p>Macrophages (F4/80<sup>+</sup>CD11b<sup>+</sup>) and regulatory T cells (CD4<sup>+</sup>CD25<sup>+</sup>Foxp3<sup>+</sup>) were immunophenotyped in white adipose tissue (WAT) from leptin receptor-deficient (db/db) mice infected with either the wild-type <i>H. pylori</i> 98–325 (white bars), the isogenic <i>H. pylori</i> 99–305 (black bars), or uninfected (dashed bars), (n = 10 mice/group). Statistically significant differences (<i>P</i><0.05) between treatments (*) are indicated.</p
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