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

Differential host response, rather than early viral replication efficiency, correlates with pathogenicity caused by influenza viruses.

Influenza viruses exhibit large, strain-dependent differences in pathogenicity in mammalian hosts. Although the characteristics of severe disease, including uncontrolled viral replication, infection of the lower airway, and highly inflammatory cytokine responses have been extensively documented, the specific virulence mechanisms that distinguish highly pathogenic strains remain elusive. In this study, we focused on the early events in influenza infection, measuring the growth rate of three strains of varying pathogenicity in the mouse airway epithelium and simultaneously examining the global host transcriptional response over the first 24 hours. Although all strains replicated equally rapidly over the first viral life-cycle, their growth rates in both lung and tracheal tissue strongly diverged at later times, resulting in nearly 10-fold differences in viral load by 24 hours following infection. We identified separate networks of genes in both the lung and tracheal tissues whose rapid up-regulation at early time points by specific strains correlated with a reduced viral replication rate of those strains. The set of early-induced genes in the lung that led to viral growth restriction is enriched for both NF-κB binding site motifs and members of the TREM1 and IL-17 signaling pathways, suggesting that rapid, NF-κB -mediated activation of these pathways may contribute to control of viral replication. Because influenza infection extending into the lung generally results in severe disease, early activation of these pathways may be one factor distinguishing high- and low-pathogenicity strains

Identifying novel transcription factors involved in the inflammatory response by using binding site motif scanning in genomic regions defined by histone acetylation

<div><p>The innate immune response to pathogenic challenge is a complex, multi-staged process involving thousands of genes. While numerous transcription factors that act as master regulators of this response have been identified, the temporal complexity of gene expression changes in response to pathogen-associated molecular pattern receptor stimulation strongly suggest that additional layers of regulation remain to be uncovered. The evolved pathogen response program in mammalian innate immune cells is understood to reflect a compromise between the probability of clearing the infection and the extent of tissue damage and inflammatory sequelae it causes. Because of that, a key challenge to delineating the regulators that control the temporal inflammatory response is that an innate immune regulator that may confer a selective advantage in the wild may be dispensable in the lab setting. In order to better understand the complete transcriptional response of primary macrophages to the bacterial endotoxin lipopolysaccharide (LPS), we designed a method that integrates temporally resolved gene expression and chromatin-accessibility measurements from mouse macrophages. By correlating changes in transcription factor binding site motif enrichment scores, calculated within regions of accessible chromatin, with the average temporal expression profile of a gene cluster, we screened for transcriptional factors that regulate the cluster. We have validated our predictions of LPS-stimulated transcriptional regulators using ChIP-seq data for three transcription factors with experimentally confirmed functions in innate immunity. In addition, we predict a role in the macrophage LPS response for several novel transcription factors that have not previously been implicated in immune responses. This method is applicable to any experimental situation where temporal gene expression and chromatin-accessibility data are available.</p></div

Top ranked motif for each of the eight expression clusters.

<p>Median fold change for each of the eight clusters is represented using blue lines and the values are shown on the left Y axes. Red lines represent Clover raw scores for the top ranked motif and the values are shown on the Y axes on the right. Based on a time-lagged correlation analysis (using optimum lag time for each motif), the correlation between the Clover score and the cluster-median expression levels are: UC1/V$CREBP1_Q2 - <i>R</i> = 0.828 (t = 2.0); UC2/V$IRF_Q6—<i>R</i> = 0.777 (t = 1.0004); UC3/V$IRF_Q6—<i>R</i> = 0.999 (t = 1.343); UC4/V$SP1_Q2_01—<i>R</i> = 0.8965 (t = 2.0); UC5/V$MYF_01—<i>R</i> = -0.900 (t = 1.001) DC1/V$NFY_01—<i>R</i> = 0.992 (t = 0.589); DC2/V$NFY_01—<i>R</i> = 0.916 (t = 2.0); DC3/V$ZFP281_01—<i>R</i> = 0.304 (t = 2.0).</p

Enrichment test results for ChIP-seq tags for specific TFs in HAc-valley regulatory elements within ±5 kbp of TSSs for genes in specific clusters.

<p>Enrichment test results for ChIP-seq tags for specific TFs in HAc-valley regulatory elements within ±5 kbp of TSSs for genes in specific clusters.</p

Correlation between Clover scores and observed TF binding.

<p>Plots show relationship between Clover scores (y axes) and ChIP-seq counts (x axes) for motifs for IRF1 (V$IRF1), IRF8 (V$IRF8) and PU.1/SPI1 (V$PU1) for all eight clusters at 0, 2 and 4 h time points. Panes A-C show Clover scores and observed counts for enriched motifs (blue diamonds), correlation for those (black line) and Clover scores and counts for motifs that are not enriched (red squares). Panes D-F show Clover scores and ChIP-seq counts for the SPI1 motif separately for each cluster (three time points for each cluster).</p

AcH4 valleys and active promoter regions.

<p>(A) AcH4 valleys. ChIP-seq signal and smoothed ChIP-seq signal are shown by gray and black lines respectively. Green bars represent the locations of detected AcH4 valleys. (B) Active promoter regions, defined as regions where detected AcH4 valleys (short blue bars shown for different time point) overlap with the ±5,000bp region around TSS (long blue bar).</p

Transcriptional response to LPS.

<p>Heatmap of five upregulated and three downregulated clusters (left) and median cluster fold change (right). Lines show median cluster expression and shaded areas show interquartile range.</p