Delineation of diverse macrophage activation programs in response to intracellular parasites and cytokines

Abstract

Background: The ability to reside and proliferate in macrophages is characteristic of several infectious agents that are of major importance to public health, including the intracellular parasites Trypanosoma cruzi (the etiological agent of Chagas disease) and Leishmania species (etiological agents of Kala-Azar and cutaneous leishmaniasis). Although recent studies have elucidated some of the ways macrophages respond to these pathogens, the relationships between activation programs elicited by these pathogens and the macrophage activation programs elicited by bacterial pathogens and cytokines have not been delineated.

Methodology/principal findings: To provide a global perspective on the relationships between macrophage activation programs and to understand how certain pathogens circumvent them, we used transcriptional profiling by genome-wide microarray analysis to compare the responses of mouse macrophages following exposure to the intracellular parasites T. cruzi and Leishmania mexicana, the bacterial product lipopolysaccharide (LPS), and the cytokines IFNG, TNF, IFNB, IL-4, IL-10, and IL-17. We found that LPS induced a classical activation state that resembled macrophage stimulation by the Th1 cytokines IFNG and TNF. However, infection by the protozoan pathogen L. mexicana produced so few transcriptional changes that the infected macrophages were almost indistinguishable from uninfected cells. T. cruzi activated macrophages produced a transcriptional signature characterized by the induction of interferon-stimulated genes by 24 h post-infection. Despite this delayed IFN response by T. cruzi, the transcriptional response of macrophages infected by the kinetoplastid pathogens more closely resembled the transcriptional response of macrophages stimulated by the cytokines IL-4, IL-10, and IL-17 than macrophages stimulated by Th1 cytokines.

Conclusions/significance: This study provides global gene expression data for a diverse set of biologically significant pathogens and cytokines and identifies the relationships between macrophage activation states induced by these stimuli. By comparing macrophage activation programs to pathogens and cytokines under identical experimental conditions, we provide new insights into how macrophage responses to kinetoplastids correlate with the overall range of macrophage activation states.

Figure 1. Comparison of transcriptional responses following infection by the intracellular pathogens Leishmania mexicana and Trypanosoma cruzi and following stimulation by LPS.

A. Unsupervised two-dimensional cluster analysis was performed on genes exhibiting statistically significant variability between the three conditions, as determined by multiclass SAM (n = 636). Replicate experiments of L. mexicana (n = 3) and T. cruzi (n = 2) infection were averaged prior to cluster analysis. B. Close-up of gene cluster upregulated by LPS and the 24 h timepoint of T. cruzi. This cluster includes many interferon-stimulated genes which are not induced by L. mexicana. C. The Venn diagram depicts the overlap of genes significantly upregulated, as determined by pairwise SAM analysis to uninfected controls, by L. mexicana, T. cruzi, LPS, both T. cruzi and LPS, both T. cruzi and L. mexicana, and both L. mexicana and LPS. There were no genes significantly upregulated by all three conditions.

Figure 2. Comparison of transcriptional response to Leishmania infection compiled from this study and previous microarray studies.

Meta-analysis was performed on the transcriptional response to Leishmania infection using data from this study and 5 additional studies. The heatmap shows genes upregulated (n = 28) and downregulated (n = 440) by Leishmania species. List of genes are shown in Dataset S2. hMDC, human monocyte-derived dendritic cells; hMDM, human monocyte-derived macrophages; mBMM, mouse bone marrow-derived macrophages; hMDMT, human monocyte-derived macrophages and T cells; mTEM, mouse thioglycollate-elicited macrophages.

Figure 3. Comparison of transcriptional response to Trypanosoma infection compiled from this study and previous microarray studies.

Meta-analysis was performed on the transcriptional response to Trypanosoma infection using data from this study and 5 additional studies. The heatmap shows genes upregulated (n = 781) and downregulated (n = 1810) by Trypanosoma species. List of genes are shown in Dataset S3. hFF, human foreskin fibroblasts; mKID, mouse whole kidney; mLIV, mouse whole liver; mSPL, mouse whole spleen; HeLa, HeLa cells; mSKN, mouse whole skin; hSMC, human vascular smooth muscle cells; hCEC, human cardiac microvascular endothelial cells; mBMM, mouse bone marrow-derived macrophages.

Figure 4. Comparison of transcriptional responses to classical activation, alternative activation, and macrophage deactivation.

A. Unsupervised two-dimensional cluster analysis was performed on genes exhibiting statistically significant variability between the three conditions, as determined by multiclass SAM (n = 1489). B. The Venn diagram depicts the overlap of genes significantly upregulated, as determined by pairwise SAM analysis to unstimulated controls, by IFNG, IL-4, IL-10, both IFNG and IL-4, both IFNG and IL-10, both IL-4 and IL-10, and all three cytokines.

Figure 5. Comparison of transcriptional responses to cytokines implicated in classical macrophage activation.

A. Unsupervised two-dimensional cluster analysis was performed on genes exhibiting statistically significant variability between stimulation with IFNG, IFNB, TNF, and IL-17, as determined by multiclass SAM (n = 773). B. The Venn diagram depicts the overlap of genes significantly upregulated, as determined by pairwise SAM analysis to unstimulated controls, by IFNG, IFNB, TNF, both IFNG and IFNB, both IFNG and TNF, both IFNB and TNF, and all three cytokines.

Figure 6. Comparison of transcriptional responses to cytokines and intracellular parasites.

Unsupervised two-dimensional cluster analysis was performed on all pathogen and cytokine arrays, using genes exhibiting statistically significant variability between these conditions, as determined by SAM (n = 5414). Arrays in red text highlight the relationship between cytokines involved in classical macrophage activation and the bacterial antigen LPS. Arrays in blue text highlight the relationship between the protozoan pathogens L. mexicana and T. cruzi and the cytokines IL-4, IL-10, and IL-17.

Figure 7. Differences in the transcriptional response in differentially derived macrophages.

A. Unsupervised two-dimensional cluster analysis was performed on genes exhibiting statistically significant variability between the 6 groups (n = 3671): IFNG stimulation of bone marrow macrophages, TNF stimulation of bone marrow macrophages, IL-4 stimulation of bone marrow macrophages, IFNG stimulation of thioglycollate macrophages, TNF stimulation of thioglycollate macrophages, and IL-4 stimulation of thioglycollate macrophages. Arrays clustered based on the type of macrophage, not the type of cytokine. B. Multiclass SAM analysis was performed on arrays representing IFNG, TNF, and IL-4 stimulation of bone marrow macrophages. Cluster analysis was performed on genes exhibiting statistically significant differences amongst bone marrow derived macrophages only (n = 168). Arrays clustered based on the type of cytokine, not the type of macrophage. C. Multiclass SAM analysis was performed on arrays representing IFNG, TNF, and IL-4 stimulation of thioglycollate macrophages. Cluster analysis was performed on genes exhibiting statistically significant differences amongst thioglycollate macrophages only (n = 124). Arrays clustered based on the type of cytokine, not the type of macrophage. D. Genes induced by IFNG in thioglycollate-elicited macrophages (TM) were compared to genes induced by IFNG in bone marrow-derived macrophages (BMM). E. Genes induced by IL-4 in TMs were compared to genes induced by IL-4 in BMMs. F. Genes induced by TNF in TMs were compared to genes induced by TNF in BMMs.