Abstract:
Understanding the interactions that occur between viruses and their hosts is important for controlling the impact of viruses on human health. 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 a computational approach that uses an integer linear program to infer the intracellular pathways through which these host factors modulate viral replication. The input is a set of viral phenotypes observed in single-host-gene mutants and a background network consisting of a variety of host cell intracellular interactions. The output is an ensemble of subnetworks that provides a consistent explanation for each gene’s role in viral replication, predicts which unassayed host factors modulate the virus, and predicts which host factors are the most direct interfaces with a viral component. The value of these inferred subnetworks is that they can be used to guide further experimentation toward uncovering and validating the mechanisms of host-virus interactions.

We analyze data from experiments screening the yeast genome for genes modulating the replication of two RNA viruses. To evaluate our method, we conduct a cross-validation experiment in which we predict whether held-aside test genes have an effect on viral replication. Our method is able to make these predictions with accuracy greater than or equal to several baseline methods that do not posit mechanistic pathways. As an additional evaluation, we use our approach to predict which unassayed host genes are likely to be involved in viral replication. Multiple predictions are supported by recent independent experimental data. (This work is a collaboration with Brandi Gancarz, Linhui Hao, Michael Ferris, Paul Ahlquist, and Mark Craven.)