Systematic Investigation of Multi-TLR Sensing Identifies Regulators of Sustained Gene Activation in Macrophages

Abstract

A typical pathogen presents a combination of Toll-like receptor (TLR) ligands during infection. Although individual TLR pathways have been well characterized, the nature of this "combinatorial code" in pathogen sensing remains unclear. Here, we conducted a comprehensive transcriptomic analysis of primary macrophages stimulated with all possible pairwise combinations of four different TLR ligands to understand the requirements, kinetics, and outcome of combined pathway engagement. We find that signal integration between TLR pathways leads to non-additive responses for a subset of immune mediators with sustained expression (>6 hr) properties and T cell polarizing function. To identify the underlying regulators, we conducted a focused RNAi screen and identified four genes-Helz2, Phf11d, Sertad3, and Zscan12-which preferentially affect the late phase response of TLR-induced immune effector expression. This study reveals key molecular details of how contemporaneous signaling through multiple TLRs, as would often be the case with pathogen infection, produce biological outcomes distinct from the single ligands typically used to characterize TLR pathways.

Keywords: Toll-like receptors; bacterial infection; innate immunity; signaling pathway crosstalk; transcriptional regulation.

Figure 1. TLR Pathway Crosstalk Enhances Cytokine Secretion beyond Maximal Single Ligand Responses

(A) Features of four representative TLR ligands selected for analysis of pathway crosstalk. (B) Mouse BMDMs were stimulated with increasing concentrations of single or dual TLR-ligand combinations of poly(I:C) and R848, as indicated in (C), for 24 hr, and cytokine secretion was measured by ELISA. Data are presented as mean ± SD of three independent experiments. Significant differences between the observed dual ligand-stimulated cytokine level and the calculated additive output of the combined single ligand values are shown. ****p < 0.0001 (two-tailed t test). (C) TLR-ligand doses used for evaluation of IL-6 and IL-12 p40 responses (Figure S2). The doses shown in red were selected for subsequent experiments.

Figure 2. Transcriptional Analysis of TLR-Ligand Combinations in Primary Macrophages Identifies Inter- and Intra-pathway MyD88 and TRIF Interactions

(A) Schematic for calculation of non-additivity (DIF value) in dual TLR-ligand transcriptional responses. Fold-inductions relative to the non-treated sample are calculated for single ligand-stimulated samples (PAMP A or PAMP B) and dual ligand-stimulated samples, with the basal expression in non-treated sample taken as 1-fold. Synergy (red) is defined as a dual ligand response more than the simple additive level of the two single ligands. Antagonism (blue) is defined as a dual ligand response less than the highest single ligand response. (B) Left: clustering of 282 transcripts with a DIF ≥2 or DIF ≤ −2 under 6 pairwise PAMP combinations at 3 time points. Antagonized/synergized fold DIF values (converted to log2 scale), are indicated by the color gradient of blue/red respectively, while additive or saturated outcomes are white. Right: percent mRNA expression normalized to the maximum level (100%) for each gene, under basal conditions, after treatment with a MyD88 ligand alone, a TRIF ligand alone, or with LPS or dual ligand. For the MyD88 alone (either R848 or Pam3CSK4) and dual ligand conditions (six pairwise PAMP combinations), the highest expression value is shown. Dual ligand combinations are shown in one-letter abbreviations as follows: I, poly(I:C); R, R848; P, Pam3CSK4; L, LPS. (C) Number of gene data points (across the 8 hr time course) showing synergy or antagonism under different dual ligand treatments. One pairwise PAMP combination at one time point is counted as one data point. (D) BMDMs from WT or the indicated strains of knockout mice were stimulated with single or dual TLR ligands (25 ng/mL R848, 10 μg/mL PIC, 10 ng/mL P3C, 5 nM Lipid A) for 24 hr, and IL-6 secretion was measured by ELISA (mean + SD). (E) qPCR validation of a subset of synergized genes showing internal synergy between the MyD88 and TRIF branches of the TLR4-LPS pathway. Left: antagonized/synergized fold DIF values are indicated by the color gradient of blue/red, respectively, while additive or saturated outcomes are white. Right: percent mRNA expression in either Myd88^−/− cells or Trif^−/− cells normalized to the maximum Lipid A response of WT cells. Data were derived from duplicate microarrays (A–C) (see STAR Methods; Figure S4; Data S2), duplicate experiments from two separate mice (D), **p < 0.01, ***p < 0.001, ****p < 0.0001 (two-tailed t test), and from duplicate qPCR experiments from two separate mice (E) (see Data S3). Ligand abbreviations: R (R848); I or PIC (poly(I:C)); P or P3C (Pam3CSK4); L (LPS).

Figure 3. Transcription Factor Motif and Pathway Analysis of TLR Non-additive Response Genes

(A and B) DiRE analysis of enriched transcriptional regulatory motifs at the promoter and promoter + distal regions of (A) synergized and (B) antagonized gene clusters as identified in Figure 2B. Members of recurrent NF-kB, AP1, IRF, STAT, and ETS transcription factor families are highlighted. (C) Ingenuity pathway analysis (IPA) functional annotation of genes within the synergized and antagonized gene clusters as identified in Figure 2B.

Figure 4. Time-Resolved Transcriptional Analysis Reveals Properties and Kinetics of Synergy

(A) Predicted and observed mRNA expression levels in BMDM stimulated with 10 μg/mL poly(I:C) + 25 ng/mL R848 for 4, 8, and 12 hr. Predicted mRNA expression was calculated by summing the net fold induction of the two single ligand stimulations for 7,342 transcripts induced in at least 2 of the 18 tested single or dual TLR-ligand conditions. Each dot represents one upregulated gene, and the predicted versus observed mRNA expression values are plotted. Red lines are the diagonal reference for additive expression if the predicted and observed mRNA levels are the same, while green lines are fitted to the observed data points. (B) mRNA expression kinetics of 86 synergized transcripts under dual and single ligand treatment. Percent mRNA expression normalized to the maximum level (100%) for each gene across all time points of the same treatment is shown in a color gradient from blue (lowest) to red (highest) expression. (C) Percent gene expression (mean ± SEM) of all 86 synergized transcripts (averaged) over a 12 hr time course of single and dual ligand treatment. (D) Distribution of dual ligand-induced gene expression synergy (DIF value) for the 86 synergized transcripts over a 12 hr time course of poly(I:C) + R848 stimulation. (E) Kinetics of synergy emergence among the 86 synergized transcripts over a 12 hr time course of poly(I:C) + R848 stimulation, as in (D). Data were derived from duplicate microarrays; see STAR Methods; Data S4 for (A) and Data S5 for (B–E). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Two-tailed t test (C) and Wilcoxon signed rank test (D).

Figure 5. A focused siRNA Screen for Regulators of Dual TLR-ligand-Induced IL-6 Secretion

(A) Selection criteria for putative regulators for the primary RNAi screen (see STAR Methods; Data S6). (B) Workflow for the RNAi screen employing three siRNA sequences per gene distributed in separate regions of duplicate 384-well plates (red circles show example of siRNA locations for a single gene). Blue region of plate, gene-specific siRNA; orange region, control siRNA. At 48 hr after reverse transfection of siRNA, mouse RAW G9 cells in duplicate plates were treated for a further 24 hr with either single or dual TLR ligands, and IL-6 secretion was measured by ELISA (see STAR Methods; Data S7). (C) Secondary RNAi screen of 24 putative hits from the primary screen. IL-6 secretion levels are shown normalized to the dual ligand response of non-target control siRNA-transfected cells. The range of expression variation for non-target control siRNA data points are shown in the blue and pink shaded regions for single and dual ligand treated cells, respectively. The data points for the three siRNAs per gene are shown as stars, circles, and triangles, with single- and dual-ligand response data point in blue and red, respectively. Representative data from one of three independent screening experiments is shown.

Figure 6. Pathway Analysis of Network Interactions among Screen Hits

(A and B) Network analysis of screen hits using IPA identifies (A) a network of hits with direct and indirect associations with IL-6 regulation, and (B) an interconnected network around the transcriptional regulators PPARG, SP1, and HNF4A. Screen hits are indicated by gray shading, genes connected to the hits are white. Hits selected for follow up are highlighted. Arrows indicate a known functional link between the genes. (C) Protein domain analysis of the screen hits HELZ2, PHF11D, ZSCAN12, and SERTAD3, conducted using SMART (http://smart.embl-heidelberg.de).

Figure 7. Screen Hits Preferentially regulate sustained TLR-Induced Transcription of Immune Effector Genes

(A) Left panel: effect of Helz2, Phf11d, Zscan12, and Sertad3 screen hit knockdown on Lipid A-induced mRNA expression of 63 immune related genes in mouse BMDM. Each hit was knocked down by two independent siRNAs in separate wells, then the BMDMs were treated with 5 nM Lipid A for 0, 1, 2, and 8 hr. mRNA levels were assayed by Fluidigm microfluidic qPCR (see STAR Methods; Data S3). Genes with consistent and significant reduction of TLR-induced expression (assessed by a two-way ANOVA analysis) by both siRNAs, in each of two replicate experiments are shown as cyan. Right panel: induction kinetics of each gene. Percent mRNA expression in 0, 1, 2, and 8 hr relative to the maximum expression (100%) is shown in a color gradient from blue (low) to red (high). (B) IPA of genes selectively affected by screen hit knockdown shows enrichment for innate/adaptive immune system communication and T cell polarization. (C–F) BMDM transfected with control or gene-specific siRNA were stimulated with Lipid A (5 nM) for 24 hr, and the indicated cytokine secretion levels were measured by ELISA. Data are mean + SD of two independent experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 (one-way ANOVA).