Systems biology and the host response to viral infection

Abstract

Will our increasing understanding of virus-host interactions translate into a new generation of antiviral therapeutics or steer us toward an expensive journey to nowhere?

Figure 1

Conceptual illustration of how various systems approaches can be applied to study virus-host interactions as a means to understand disease biology through model systems and chemical or genetic perturbations that would provide insights into a repertoire of novel targets for therapeutic intervention strategies, host factors for biomarker discovery and viral determinants for diagnostics.

Figure 2. Example highlighting the distinct mechanisms of antiviral actions by pharmacological inhibitors of cellular cyclin-dependent kinases (CDKs) compared to conventional antiviral drugs.

The illustration depicts the viral functions that are directly repressed by CDK inhibitors or by two typical antiviral drugs, acyclovir and foscarnet. Because CDKs are required for transcription of HSV immediate-early (IE), early (E) and perhaps late (L) genes, and for viral DNA replication, CDK inhibitors can target several stages of the viral replication cycle as opposed to blocking a specific viral target(s), such as the viral thymidine kinase (TK) or DNA polymerase (DNA pol). Thus, a single drug targeting a single cellular protein that is required for several viral functions is akin to combination antiviral therapy, in which each drug targets a different viral enzymes. Hel./prim, helicase-primase. Reproduced with permission from Luis Schang.

Figure 3. Functional relationships of activated of cell death responses during r1918 influenza virus infection.

Biological network of selected genes that were induced at least twofold (P < 0.01) in r1918-infected mouse lung as compared with uninfected controls. This diagram shows the direct (solid lines) and indirect (dashed lines) interactions reported for these cell death– (blue shading) and immune response–related genes (yellow shading); gray denotes genes with multiple and/or undefined biological function. Biological network analysis was performed using the Ingenuity Systems (Redwood City, CA) Ingenuity Pathway Analysis program and showed statistical significance when assessed using Fisher's exact test, which was used to calculate a P value determining the probability that each biological function and/or disease assigned to that dataset was due to chance alone.