Applications of high-throughput genomics to antiviral research: evasion of antiviral responses and activation of inflammation during fulminant RNA virus infection

Abstract

Host responses can contribute to the severity of viral infection, through the failure of innate antiviral mechanisms to recognize and restrict the pathogen, the development of intense systemic inflammation leading to circulatory failure or through tissue injury resulting from overly exuberant cell-mediated immune responses. High-throughput genomics methods are now being used to identify the biochemical pathways underlying ineffective or damaging host responses in a number of acute and chronic viral infections. This article reviews recent gene expression studies of 1918 H1N1 influenza and Ebola hemorrhagic fever in cell culture and animal models, focusing on how genomics experiments can be used to increase our understanding of the mechanisms that permit those viruses to cause rapidly overwhelming infection. Particular attention is paid to how evasion of type I IFN responses in infected cells might contribute to over-activation of inflammatory responses. Reviewing recent research and describing how future studies might be tailored to understand the relationship between the infected cell and its environment, this article discusses how the rapidly growing field of high-throughput genomics can contribute to a more complete understanding of severe, acute viral infections and identify novel targets for therapeutic intervention.

Fig. 1

Activation of inflammatory and type I IFN-regulated genes in 1918 influenza virus-infected mouse lung tissue. Gene expression profiles in the lungs of mice infected with equivalent doses the reconstructed 1918 virus (r1918), a chimeric virus expressing either the 1918 HA and NA genes (2:6) or the 1918 HA, NA, M, NP and NS genes (5:3) compared to a contemporary H1N1 human-adapted influenza virus A/Texas/36/91 (Tx91). (Top panel) expression of inflammatory cytokine genes, (bottom panel) expression of type I IFN-regulated genes. For each infection point, the data presented are the error-weighted average expression changes calculated from four technical replicate arrays performed on three individual mice (n = 12 total). Genes shown in red were up-regulated and genes shown in green were down-regulated in infected relative to mock-infected mouse lung. At right is shown the lung pathology at 3 days post-infection. Modified from Kash et al. (2006c) (data available at http://viromics.washington.edu/publications.html).

Fig. 2

Expression of IFN-alpha and type I IFN-regulated genes in 1918 influenza virus-infected macaque bronchus. Gene expression profiles in bronchus tissue isolated from macaques infected with equivalent doses of the reconstructed 1918 virus (r1918) compared to a H1N1 human-adapted influenza virus A/Kawasaki/1/73 (K173). For each infection point, the data presented are the error-weighted average expression changes calculated from four technical replicate arrays performed on one to three individual macaques (n = 4–12 total). Genes shown in red were up-regulated and genes shown in green were down-regulated in infected relative to mock-infected macaque lung. Modified from Kobasa et al. (2007) (data available at http://viromics.washington.edu/publications.html).

Fig. 3

Expression of unannotated genes in lung tissue isolated from mice infected with equivalent doses the reconstructed 1918 virus (r1918) compared to a H1N1 human-adapted influenza virus A/Texas/36/91 (Tx91). For each infection point, the data presented are the error-weighted average expression changes calculated from four technical replicate arrays performed on three individual mice (n = 12 total). Adapted from Kash et al. (2006c) (data available at http://viromics.washington.edu/publications.html).

Fig. 4

Expression of genes with unknown or unannotated function during Ebola and Marburg virus infection and type I IFN treatment. Expression of unannotated genes in mock, Zaire ebolavirus (ZEBOV), Marburvirus (MARV) or Reston ebolavirus (REBOV)-infected Huh7 cells treated with IFNα-2b for 24 h. For each infection time point, the data presented are the average expression from four technical replicate arrays performed on a pool of RNA from two independent experiments (n = 4 total). Adapted from Kash et al. (2006b) (data available at http://viromics.washington.edu/publications.html).