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PLoS Comput Biol. 2014 May 29;10(5):e1003626. doi: 10.1371/journal.pcbi.1003626. eCollection 2014 May.

Inferring host gene subnetworks involved in viral replication.

Author information

1
Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
2
Luminex Corporation, Madison, Wisconsin, United States of America; Institute for Molecular Virology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
3
Institute for Molecular Virology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America; Howard Hughes Medical Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
4
Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
5
Institute for Molecular Virology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America; Howard Hughes Medical Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America; Morgridge Institute for Research, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

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/.

PMID:
24874113
PMCID:
PMC4038467
DOI:
10.1371/journal.pcbi.1003626
[Indexed for MEDLINE]
Free PMC Article
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