Abstract
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 http://www.biostat.wisc.edu/~craven/chasman_host_virus/.
Highlights
A virus requires host cellular machinery to complete its life cycle
The value of these inferred subnetworks is that they can be used to (i) predict which unassayed genes may be involved in viral replication, (ii) interpret the role of each hit in modulating the virus, and (iii) guide further experimentation that is aimed at uncovering and validating the mechanisms of host-virus interaction
Genome-wide suppression experiments have been used to identify individual host genes whose products are involved in viral replication
Summary
Understanding the interactions that occur between viruses and their hosts can contribute to the development of preventative and therapeutic methods to control their effects on human health To this end, an increasing number of genome-wide loss-offunction studies have recently been performed to identify host factors that modulate the virus life cycle in a host cell. For each host gene that is manipulated, the effect on the virus is assessed by measuring the replicative yield of viral genetic material or viral proteins relative to a control. These genome-wide screens identify a large number of host genes, which we refer to as hits, whose loss has a significant effect on the virus. The exception is the metabolic pathway interactions, which are edges between enzymes that catalyze adjacent metabolic reactions
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