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

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

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

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

Clover scores and ChIP-seq counts for TF when motif is not over represented.

<p>An example of observed counts (pane A) and predicted scores (pane B) for TF whose motif was not found to be over represented. Top graph shows normalized counts for IRF1 within the HAc-valley regulatory elements of genes in UC1 (purple line), or within 10kb region centered at TSS for the same genes (green line). Graph below shows predicted binding of those IRF1 as represented by Clover raw scores (red line) superimposed on the UC1 cluster median fold change (blue line).</p

An example of good correlation between predicted and measured TF binding (for the cluster UC3).

<p>Top 3 graphs show normalized counts for IRF1, IRF8 and SPI1 within the HAc-valley regulatory elements of genes in DC3 (purple line), or within 10kb region centered at TSS for the same genes (green line). Graphs below show predicted binding of those TFs as represented by Clover raw scores (red lines) superimposed on the UC3 cluster median fold change (blue lines).</p

Clover scores and ChIP-seq counts for SMAD1.

<p>Median fold change for clusters is shown by blue lines for UC1 (upper pane) and UC3 (lower pane) and the values are shown on the left Y axes. Red lines represent Clover raw scores for V$SMAD1_01 motif and the values are shown on the Y axes on the right.</p

Decreased TRAF6 levels are sufficient to impair host control of influenza virus replication.

<p>(<i>A</i>) Western blotting of TRAF6, IRF7, and β-actin was performed using protein lysates from the indicated LET1 cells. Densitometric quantification of 3–4 independent experiments was performed and means ±SEM normalized to actin and relative to control shRNA cells is as follows: TRAF6 Western. Control shRNA, 1; TRAF6 shRNA, 0.43±0.15; miR-144, 0.52±0.12; miR-451, 0.79±0.12. IRF7 Western. Control shRNA, 1; TRAF6 shRNA, 0.47±0.12; miR-144, 0.68±0.09; miR-451, 1.16±0.26. (<i>B</i>) Gene expression by qRT-PCR in LET1 cells stably expressing TRAF6-specific shRNAs and infected with influenza virus for 18 h is shown relative to the level in infected LET1 cells expressing control shRNA. Means ±SEM for 3 independent experiments performed in duplicate are shown. (<i>C</i>) qRT-PCR of influenza viral load in LET1 cells expressing TRAF6-specific or control shRNAs or in the cell-free supernatants 18 h post-infection. Means ± SEM for 3 independent experiments performed in duplicate are shown. MOI = 5; * p<0.05, ** p<0.01. (<i>D</i>) Cells infected as described in <i>C</i> were stained for viral NP protein and analyzed by flow cytometry. Data are representative of 2 independent experiments. (<i>E</i>) Heat map of antiviral gene expression generated from the log₂ ratios of gene expression between the following cells: miR-144^-/-/miR-144^+/+ type I lung epithelial cells, TC-1+miR-144+miR-451/TC-1+vector alone, LET1+miR-144/LET1+vector alone, IRF7null vs WT lungs, LET1+TRAF6 shRNAs/LET1+control shRNA. All cells were infected with PR8 influenza virus for 24 h, gene expression measured by qRT-PCR, and mean intensities for 3 independent samples compared. Red (blue) represents up-(down-) regulation relative to controls. (<i>F</i>) Network model for miR-144 regulation of the TRAF6-IRF7-antiviral network.</p