Graphlets, automorphism orbits, and GDVs.

<p>(<b>A</b>) All 9 graphlets with 2, 3 and 4 nodes, denoted by , ,…,; they contain 15 topologically unique node types, called automorphism orbits, denoted by 0, 1, 2, …, 14. In a particular graphlet, nodes belonging to the same orbit are of the same shade (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023016#pone.0023016-Prulj2" target="_blank">[47]</a> for details). (<b>B</b>) An illustration of the GDV of node ; it is presented in the table for orbits 0 to 14: is touched by 4 edges (orbit 0), end-nodes of 2 graphlets (orbit 1), etc. The figure is taken from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023016#pone.0023016-Milenkovi3" target="_blank">[53]</a>.</p

The overlap of BC genes from the four categories in the human PPI network.

<p>The overlap of BC genes from the four categories in the human PPI network.</p

The top 1% (i.e., 91) GDC-central genes.

<p>If a gene is an aging (“A”), cancer (“C”), HIV (“HIV”), or pathogen-interacting (“PI”) gene, there is an “X” in the corresponding entry.</p

Overlap of the three DSs created by DS-RAI, DS-DC, and DS-GDC algorithms applied to the human PPI network.

<p>Overlap of the three DSs created by DS-RAI, DS-DC, and DS-GDC algorithms applied to the human PPI network.</p

An illustration of DSs in a toy network.

<p>The DSs were obtained by (<b>A</b>) DS-RAI and (<b>B</b>) DS-DC algorithms. The example in panel A is taken from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023016#pone.0023016-Rai1" target="_blank">[57]</a>, and the authors describe the algorithm as follows. In phase 1, nodes 1, 4, 8, 12, and 16 are colored black as members of an independent DS. In phase 2, nodes 2, 9, and 11 are colored dark grey as connectors that connect nodes in the independent DS resulting from phase 1. In phase 3, the connected DS resulting from phase 2 is pruned to reduce it size by removing node 16 from the DS (no other nodes can be removed without violating the requirement of producing a connected DS of the graph). In panel B, all nodes are initially in the DS and then nodes are visited in order of their increasing degrees and removed from the DS if the resulting DS is a valid connected DS of the graph. That is, nodes are removed in the following order: 3, 16, 2, 4, 7, 10, 13, 14, 15, and 9. The resulting DS therefore contains the remaining nodes: 1, 5, 6, 8, 11, and 12. Clearly, the DS produced by DS-DC (black nodes in panel B) is smaller than the DS produced by DS-RAI (black and dark grey nodes in panel A).</p

The performance of GDC and its comparison with other centrality measures.

<p>(<b>A</b>) Enrichments in BC genes of the top % of the most GDC-central genes (denoted by “Central”, blue bars) and all remaining genes (denoted by “Non-central”, red bars) in the human PPI network. (<b>B</b>) Enrichment in drug targets of BC genes that are GDC-central (“Central”) and BC genes that are non-GDC-central (“Non-central”). (<b>C</b>) Enrichments in BC genes of the top % of the most central genes in the human PPI network, with respect to the four centrality measures (DC, BWC, SC, and GDC), broken into the four BC gene categories (aging (A), cancer (C), HIV (HIV), and pathogen-interacting (PI) genes). In all panels, the values of where precision and recall cross (as illustrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023016#pone-0023016-g005" target="_blank">Figure 5</a>) are used; equals 3, 10, 12, and 6, for A, C, HIV, and PI genes, respectively, for each of the four centrality measures.</p

An illustration of the differences between DC and GDC.

<p><i>Left:</i> Direct neighborhood of ZAP90, a cancer and HIV gene, in the human PPI network <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023016#pone.0023016-Radivojac1" target="_blank">[28]</a>. Its degree is 48 and it is ranked as the top gene with respect to DC. <i>Right:</i> Direct neighborhood of PRKACA, an HIV gene, in the network. Its degree is 145 and it is ranked as the top gene with respect to DC. Both proteins have the same GDC and are ranked as top genes with respect to GDC. Hence, GDC rewards the ranking of a low-degree gene if its 4-deep neighborhood is dense (ZAP90) and penalizes the ranking of a high-degree gene if its 4-deep neighborhood is sparse (PRKACA). (For the esthetics of the figure, we only show 1-deep neighborhoods.)</p

List of proteins evaluated in high-throughput yeast two-hybrid assay and number of host protein-protein interactions.

<p>List of proteins evaluated in high-throughput yeast two-hybrid assay and number of host protein-protein interactions.</p

Host pathways targeted by <i>Coxiella</i>.

<p><i>C</i>. <i>burnetii</i>-interacting host proteins are present in interconnected Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways with the potential to affect multiple cellular processes of the host. The pathways are grouped into five major categories: RNA processing, protein processing, degradation pathways, signaling (including signaling events related to the immune response), and metabolism. The size of a star indicates the number of targeted host proteins in each pathway. ECM, extracellular matrix; ER, endoplasmic reticulum; ErbB, erythroblastic leukemia viral oncogene; ESCRT, endosomal sorting complexes required for transport; MAPK, mitogen-activated protein kinase; NOD, nucleotide-binding oligomerization domain; PIK3, phosphatidylinositol-3-kinases; TCA, tricarboxylic acid; TGF, transforming growth factor.</p

Mechanisms of action of <i>Coxiella burnetii</i> effectors inferred from host-pathogen protein interactions

<div><p><i>Coxiella burnetii</i> is an obligate Gram-negative intracellular pathogen and the etiological agent of Q fever. Successful infection requires a functional Type IV secretion system, which translocates more than 100 effector proteins into the host cytosol to establish the infection, restructure the intracellular host environment, and create a parasitophorous vacuole where the replicating bacteria reside. We used yeast two-hybrid (Y2H) screening of 33 selected <i>C</i>. <i>burnetii</i> effectors against whole genome human and murine proteome libraries to generate a map of potential host-pathogen protein-protein interactions (PPIs). We detected 273 unique interactions between 20 pathogen and 247 human proteins, and 157 between 17 pathogen and 137 murine proteins. We used orthology to combine the data and create a single host-pathogen interaction network containing 415 unique interactions between 25 <i>C</i>. <i>burnetii</i> and 363 human proteins. We further performed complementary pairwise Y2H testing of 43 out of 91 <i>C</i>. <i>burnetii-</i>human interactions involving five pathogen proteins. We used the combined data to <i>1</i>) perform enrichment analyses of target host cellular processes and pathways, <i>2</i>) examine effectors with known infection phenotypes, and <i>3</i>) infer potential mechanisms of action for four effectors with uncharacterized functions. The host-pathogen interaction profiles supported known <i>Coxiella</i> phenotypes, such as adapting cell morphology through cytoskeletal re-arrangements, protein processing and trafficking, organelle generation, cholesterol processing, innate immune modulation, and interactions with the ubiquitin and proteasome pathways. The generated dataset of PPIs—the largest collection of unbiased <i>Coxiella</i> host-pathogen interactions to date—represents a rich source of information with respect to secreted pathogen effector proteins and their interactions with human host proteins.</p></div