A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel "crowd-based" approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse 'omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models

A comprehensive collection of systems biology data characterizing the host response to viral infection

The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). By comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection

Model system comparison based on Inferelator regression models.

<p>Regulatory influence models for each gene cluster of both viruses were applied to comparable datasets from distinct model systems. For SARS-CoV, regulatory influences inferred from Calu3 data were applied to SARS-CoV infection data from a primary human airway epithelial cell model system. For influenza, the Calu3 model was applied to influenza infection data from C57BL/6 mice. The observed gene expression profile of the non-Calu3 data clusters was compared to the predicted gene expression profile based on the Calu3 model. Correlations were calculated for this comparison from each cluster and are shown in A. In B, a sample expression profile from a highly-predictive cluster from each virus is shown with the observed non-Calu3 expression profile shown in red, compared to the predicted expression profile from the Calu3 model in green. In C, the average cluster correlation for the SARS-CoV and influenza comparisons is shown, in comparison to the correlation obtained from applying 100 random models to the corresponding alternative model system. P-values were obtained by comparing each correlation with the distribution of 100 correlations based on random models.</p

Conserved regulators from influenza virus.

<p>Bold: genes overlapping with the SARS-CoV list.</p

Top genes from SARS-CoV prioritization.

<p>Top genes from SARS-CoV prioritization.</p

Inferred network edge validation.

<p>A) Network edges were compared to a predicted transcription factor – target database. The number of transcriptome network edges for each virus that was also present in the database (red) was compared with the number of matching edges in 1000 random networks (gray) to estimate the number of matching edges expected from chance. B) Relationships between genes targeted in a large siRNA-targeting study <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069374#pone.0069374-Hurley1" target="_blank">[19]</a> and the downstream affected genes were compared to relationships predicted from our network inference approach. Results show the number of genes that exhibited statistically significant overlap between their network neighbors and perturbed genes from the siRNA targeting study. Red designates the overlap with neighbors from the actual network; grey designates overlap with neighbors from 500 random networks (see Materials and Methods). Error bars represent standard deviation of the distribution of gene percentages with significant overlaps.</p

GSEA-based enrichment analysis of SARS-CoV rankings.

<p>Seven rankings of genes from the SARS-CoV network were assessed for enrichment as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069374#pone-0069374-g005" target="_blank">figure 5</a>, this time using the 299 gene sets from the Molecular Signatures Database matching the search keys “viral” or “virus”. Average scores are compared to random rankings. Double stars indicate p-values <0.001.</p

Top genes from influenza regulatory prioritization.

<p>Top genes from influenza regulatory prioritization.</p

Overlap between rankings.

<p>Genes were ranked according to betweenness, degree and differential expression (DE) as described in Materials and Methods. Venn diagrams indicate the overlap in the top 10% of each of these rankings for both viruses as indicated.</p

Network Terminology.

<p>Association networks capture both physical and regulatory interactions between gene pairs. Network hubs are identified by the degree centrality metric, which is the number of edges (i.e. relationships, represented by connecting lines) associated with any given vertex (elements being connected, e.g. genes, identified as circles). Network bottlenecks have high values for the betweenness centrality metric, which is the number of shortest paths between all pairs of vertices that pass through a given vertex. Network neighbors are vertices connected by a single edge.</p