Inferring Host Gene Subnetworks Involved in Viral Replication

<div><p>Systematic, genome-wide loss-of-function experiments can be used to identify host factors that directly or indirectly facilitate or inhibit the replication of a virus in a host cell. We present an approach that combines an integer linear program and a diffusion kernel method to infer the pathways through which those host factors modulate viral replication. The inputs to the method are a set of viral phenotypes observed in single-host-gene mutants and a background network consisting of a variety of host intracellular interactions. The output is an ensemble of subnetworks that provides a consistent explanation for the measured phenotypes, predicts which unassayed host factors modulate the virus, and predicts which host factors are the most direct interfaces with the virus. We infer host-virus interaction subnetworks using data from experiments screening the yeast genome for genes modulating the replication of two RNA viruses. Because a gold-standard network is unavailable, we assess the predicted subnetworks using both computational and qualitative analyses. We conduct a cross-validation experiment in which we predict whether held-aside test genes have an effect on viral replication. Our approach is able to make high-confidence predictions more accurately than several baselines, and about as well as the best baseline, which does not infer mechanistic pathways. We also examine two kinds of predictions made by our method: which host factors are nearest to a direct interaction with a viral component, and which unassayed host genes are likely to be involved in viral replication. Multiple predictions are supported by recent independent experimental data, or are components or functional partners of confirmed relevant complexes or pathways. Integer program code, background network data, and inferred host-virus subnetworks are available at <a href="http://www.biostat.wisc.edu/~craven/chasman_host_virus/" target="_blank">http://www.biostat.wisc.edu/~craven/chasman_host_virus/</a>.</p></div

Phenotype labels for suppressed host genes.

<p>Distribution of phenotype labels for genes in the background network. The labels were derived from genome-wide assays of Brome Mosaic Virus and Flock House Virus replication in yeast.</p

The steps of our subnetwork inference approach.

<p>Each edge is shown with a numeric identifier for cross-reference. (<b>A</b>) Add a new node to the background network, representing the virus. Add connections between all nodes except <b>no-effects</b> to the new virus node, representing the possibility of any host factor having a direct interaction with a viral component. (<b>B</b>) For each hit identified by the genome-wide mutant assay, enumerate candidate paths through the background network that could explain it by providing a linear path to the virus node. (<b>C</b>) Infer an ensemble of consistent subnetworks. Each subnetwork is a union of paths that accounts for all of the hits and is consistent with virus phenotype data.</p

A component from the inferred subnetwork ensemble showing a connection between Acb1 and the literature-extracted ubiquitin-proteasome-system interactions.

<p>All node and edge predictions shown have confidence = 1.0 in the ensemble. A dashed edge with no terminal indicates connections to the rest of the subnetwork. Edges extracted from literature are colored blue. Doubled blue edges (as from Rsp5p to Spt23p) indicate literature-extracted edges that were also present in the original background network. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003626#pcbi-1003626-g001" target="_blank">Figure 1</a> for a key to the other network elements.</p

A component from the inferred subnetwork ensemble showing a connection between the literature-identified interface Ydj1p and two hits, Hsf1p and Ure2p.

<p>The blue edge from Ydj1p to the virus was originally extracted from literature. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003626#pcbi-1003626-g001" target="_blank">Figure 1</a> for a key to the other network elements.</p

A component from the inferred subnetwork ensemble showing the predicted involvement of Snf7p and Vps4p in viral replication.

<p>For predictions made about node and edge relevance, confidence values <1.0 are indicated. For the unassayed nodes, the same phenotype label prediction was made in all solutions in which they appear; similarly, all solutions predicted the same direction for the undirected edges. Dashed edges indicate cases in which the edge's direction was not fixed in the background network. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003626#pcbi-1003626-g001" target="_blank">Figure 1</a> for a key to the other network elements.</p

Gene Ontology terms represented by both experimental and predicted BMV hits.

<p>GO terms that MGSA returns for both BMV experimental hits alone and for the inferred BMV subnetwork (with probability ). In the “Experimental hits” column are shown the number of hits associated with the GO term, and MGSA's probability that the GO term explains the experimental hit set alone. In the “Predicted hits” column are shown the number of predicted hits associated with the GO term, and MGSA's probability that the GO term explains the combined experimental and predicted hit set. The column “<i>p</i>-value” shows the proportion of random subnetworks for which the MGSA probability of the GO term is greater than or equal to that of the inferred subnetwork; asterisks indicate .</p

Accuracy-coverage curves for the sign-prediction task.

<p>BMV on the left, FHV on the right. The horizontal line indicates the accuracy that would be achieved by assigning the plurality phenotype label to every test case (<b>down</b> for BMV, <b>up</b> for FHV.)</p

Input and output for our subnetwork inference approach.

<p>(<b>A</b>) The inputs to our subnetwork inference approach are phenotypes measured in a loss-of-function assay and a background network characterizing known interactions. (<b>B</b>) The network elements represented in panels <b>A</b>, <b>C</b>, and other figures. (<b>C</b>) An inferred subnetwork for the given inputs. The subnetwork includes a directed, consistent path linking each hit (gene with an <b>up</b> or <b>down</b> phenotype) to the virus. The red borders on the unassayed nodes G and H indicate that they are inferred to have the <b>down</b> phenotype. Edges shown in gray are not included in the subnetwork.</p

Additional Gene Ontology terms represented by the inferred BMV subnetwork.

<p>GO terms that MGSA returns for the inferred BMV subnetwork, but not for the BMV experimental hits alone (with probability ). In the “Experimental hits” column are shown the number of hits associated with the GO term, and MGSA's probability that the GO term explains the experimental hit set alone. In the “Predicted hits” column are shown the number of predicted hits associated with the GO term, and MGSA's probability that the GO term explains the combined experimental and predicted hit set. The column “<i>p</i>-value” shows the proportion of random subnetworks for which the MGSA probability of the GO term is greater than or equal to that of the inferred subnetwork; asterisks indicate .</p