The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus

Abstract

Background: The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ anti-immune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation.

Results: We validated our predictions in 18 different knockout (KO) mouse strains, showing that network topology provides significant predictive power to identify genes that are important for viral infection. We identified a novel player in the immune response to virus infection, Kepi, an inhibitory subunit of the protein phosphatase 1 (PP1) complex, which protects against SARS-CoV pathogenesis. We also found that receptors for the proinflammatory cytokine tumor necrosis factor alpha (TNFα) promote pathogenesis, presumably through excessive inflammation.

Conclusions: The current study provides validation of network modeling approaches for identifying important players in virus infection pathogenesis, and a step forward in understanding the host response to an important infectious disease. The results presented here suggest the role of Kepi in the host response to SARS-CoV, as well as inflammatory activity driving pathogenesis through TNFα signaling in SARS-CoV infections. Though we have reported the utility of this approach in bacterial and cell culture studies previously, this is the first comprehensive study to confirm that network topology can be used to predict phenotypes in mice with experimental validation.

Keywords: Network; Pathogenicity; SARS coronavirus; Systems biology.

Fig. 1

Topological rankings work better to predict mouse phenotype than differential expression or expert selection. The ability of each method to correctly classify genes as having a significant effect on pathogenesis as determined by weight loss different than wild-type mice infected with SARS-CoV (see Table 1) was assessed using a receiver-operator characteristic curve (ROC). The area under the curve (AUC) is shown in the legend. The differential expression (DE) category indicates the range of AUC values obtained when genes were ranked by DE from all viral dose and day post-infection combinations

Fig. 2

Kepi and TNFRs exhibit opposing effects on pathogenesis of SARS-CoV. C57BL/6 J mice lacking Kepi or Tnfrsf1a/b were infected with SARS-CoV at varying 10³ PFU. Weight loss expressed as the mean percentage of starting weight for five mice per time point up to 4 days post-infection, and three mice for 5–7 days post-infection is shown (y axis) plotted over time post-infection (x axis). Error bars indicate standard error of the means

Fig. 3

Validation of network predictions. Network neighborhoods for each of the target gene deletions tested were assessed for their expression difference from the rest of the network using a Student’s t test. Neighborhoods are defined in terms of the WGCNA module that contains the target gene (blue bars) or the first-order network of the target gene from the CLR-inferred network (red bars). All comparisons shown have p values less than 0.001. Standard error is shown for each data point as error bars. Negative mean expression indicates that deletion of the target gene is reducing the expression of its neighborhood compared to response in a wild type mouse

Fig. 4

Fold changes in mut/WT for cluster categories and GO terms. a Expression data of mutant and WT mice from infection with SARS-CoV were analyzed using hierarchical clustering; the functional content of these clusters was determined using functional enrichment analysis. The average direction of fold change elicited by the mutant for the cluster are shown coupled with p-values for the significance of the change. P-values were calculated using permutation tests of random gene groups having identical sizes to the gene group under test. For (b), values were calculated as in (a), except genes were grouped for fold change analysis using selected GO terms instead of gene expression clusters. General apoptosis = GO:0006915, neutrophil apoptosis = GO:0001781, GO:0033029, GO:0033030, GO:0033031, GO:0006925, inflammation = GO:0006954

Fig. 5

Kepi and TNFα signaling during SARS-CoV infection. Proposed model for the influence of Kepi, PP1, and TNFα signaling on SARS-CoV-mediated lung pathogenesis during WT conditions (a), Kepi deletion (b), and Tnfrsf1a/1b deletion (c). Bold lines indicate strong effects, thin lines indicate weak or diminished effects; dashed lines indicate indirect effects