The Human-Bacterial Pathogen Protein Interaction Networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis

Bacillus anthracis, Francisella tularensis, and Yersinia pestis are bacterial pathogens that can cause anthrax, lethal acute pneumonic disease, and bubonic plague, respectively, and are listed as NIAID Category A priority pathogens for possible use as biological weapons. However, the interactions between human proteins and proteins in these bacteria remain poorly characterized leading to an incomplete understanding of their pathogenesis and mechanisms of immune evasion.In this study, we used a high-throughput yeast two-hybrid assay to identify physical interactions between human proteins and proteins from each of these three pathogens. From more than 250,000 screens performed, we identified 3,073 human-B. anthracis, 1,383 human-F. tularensis, and 4,059 human-Y. pestis protein-protein interactions including interactions involving 304 B. anthracis, 52 F. tularensis, and 330 Y. pestis proteins that are uncharacterized. Computational analysis revealed that pathogen proteins preferentially interact with human proteins that are hubs and bottlenecks in the human PPI network. In addition, we computed modules of human-pathogen PPIs that are conserved amongst the three networks. Functionally, such conserved modules reveal commonalities between how the different pathogens interact with crucial host pathways involved in inflammation and immunity.These data constitute the first extensive protein interaction networks constructed for bacterial pathogens and their human hosts. This study provides novel insights into host-pathogen interactions

Network properties of interacting proteins.

<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.

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

Inparanoid ortholog groups.

<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.

<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

GSEA results.

<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.

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

Clinical Validation of a Proteomic Biomarker Threshold for Increased Risk of Spontaneous Preterm Birth and Associated Clinical Outcomes: A Replication Study

Preterm births are the leading cause of neonatal death in the United States. Previously, a spontaneous preterm birth (sPTB) predictor based on the ratio of two proteins, IBP4/SHBG, was validated as a predictor of sPTB in the Proteomic Assessment of Preterm Risk (PAPR) study. In particular, a proteomic biomarker threshold of −1.37, corresponding to a ~two-fold increase or ~15% risk of sPTB, significantly stratified earlier deliveries. Guidelines for molecular tests advise replication in a second independent study. Here we tested whether the significant association between proteomic biomarker scores above the threshold and sPTB, and associated adverse outcomes, was replicated in a second independent study, the Multicenter Assessment of a Spontaneous Preterm Birth Risk Predictor (TREETOP). The threshold significantly stratified subjects in PAPR and TREETOP for sPTB (p = 0.041, p = 0.041, respectively). Application of the threshold in a Kaplan–Meier analysis demonstrated significant stratification in each study, respectively, for gestational age at birth (p &lt; 001, p = 0.0016) and rate of hospital discharge for both neonate (p &lt; 0.001, p = 0.005) and mother (p &lt; 0.001, p &lt; 0.001). Above the threshold, severe neonatal morbidity/mortality and mortality alone were 2.2 (p = 0.0083,) and 7.4-fold higher (p = 0.018), respectively, in both studies combined. Thus, higher predictor scores were associated with multiple adverse pregnancy outcomes

Interactions with host innate immune response.

<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