In atherosclerotic arteries, blood monocytes differentiate to macrophages in the presence of growth factors, such as macrophage colony-stimulation factor (M-CSF), and chemokines, such as platelet factor 4 (CXCL4). To compare the gene expression signature of CXCL4-induced macrophages with M-CSF–induced macrophages or macrophages polarized with IFN-γ/LPS (M1) or IL-4 (M2), we cultured primary human peripheral blood monocytes for 6 d. mRNA expression was measured by Affymetrix gene chips, and differences were analyzed by local pooled error test, profile of complex functionality, and gene set enrichment analysis. Three hundred seventy-five genes were differentially expressed between M-CSF– and CXCL4-induced macrophages; 206 of them overexpressed in CXCL4 macrophages coding for genes implicated in the inflammatory/immune response, Ag processing and presentation, and lipid metabolism. CXCL4-induced macrophages overexpressed some M1 and M2 genes and the corresponding cytokines at the protein level; however, their transcriptome clustered with neither M1 nor M2 transcriptomes. They almost completely lost the ability to phagocytose zymosan beads. Genes linked to atherosclerosis were not consistently upregulated or downregulated. Scavenger receptors showed lower and cholesterol efflux transporters showed higher expression in CXCL4- than M-CSF–induced macrophages, resulting in lower low-density lipoprotein content. We conclude that CXCL4 induces a unique macrophage transcriptome distinct from known macrophage types, defining a new macrophage differentiation that we propose to call M4.

The mononuclear phagocyte system is essential to the innate immune response and encompasses various types of constitutive tissue macrophages (e.g., Kupffer cells in the liver or alveolar macrophages in the lung). Under inflammatory conditions, macrophages can differentiate from peripheral blood monocytes under the influence of various growth factors, cytokines, or infectious agents (1). In atherosclerosis, macrophage differentiation is critically related to disease progression. During atherogenesis, blood monocytes are thought to enter the arterial wall and differentiate into macrophages, which sustain an inflammatory milieu and promote plaque formation (25).

As demonstrated by in vitro and in vivo data, macrophages present in chronically inflamed tissues can assume different phenotypes. The best defined polarization types are M1 and M2 (6). According to the classical paradigm, M1 macrophages can be obtained through activation by IFNγ, TNF-α, or LPS, whereas the alternative M2 macrophages can be induced through activation by IL-4, IL-10, or IL-13 (7, 8). The phenotypes of macrophages in vivo are incompletely described, and M1 and M2 are probably not the only macrophage phenotypes present in vivo.

In atherosclerosis, there is evidence for the presence of several different macrophage phenotypes within atherosclerotic plaques, some with features of M1 and M2 (9). In addition, other differentiation types like CD14CD68+ and CD14+CD68 macrophages have been identified in coronary artery lesions (10). In vivo, differentiation of macrophages toward different phenotypes has been associated with certain drugs, growth factors, and other mediators. M2 differentiation is induced by PPARγ agonists (9), whereas M-CSF preferentially induces CD14+CD68+ macrophages (10) and hemoglobin-haptoglobin promotes differentiation toward CD163highHLA-DRlow macrophages (11).

Only two growth factors are known to promote differentiation of monocytes into macrophages in vitro: M-CSF (12) and platelet factor-4 (CXCL4) (13). M-CSF has been shown to induce a transcriptome that is similar to that of M2 macrophages (14). The physiologic role and function of M-CSF has been thoroughly studied. Knockout mice lacking M-CSF (CSF1) or its receptor (CSF1R) are protected from atherogenesis (15, 16). By contrast, the role of the platelet chemokine CXCL4 is far more enigmatic. CXCL4 strongly suppresses megakaryocyte differentiation (17), inhibits monocyte apoptosis, and promotes macrophage differentiation (13). CXCL4 is released from platelets upon activation in micromolar concentrations and has a broad range of biologic functions, including induction of respiratory burst in human monocytes accompanied by secretion of several chemokines such as CCL3, CCL4, and CXCL8 (1820). In vivo, the presence of CXCL4 within atherosclerotic lesions has been shown to correlate with clinical parameters (21). Eliminating the PF4 gene coding for CXCL4 by homologous recombination has been shown to reduce lesion formation in a mouse model of atherosclerosis (22).

Although the transcriptomes of M-CSF–induced macrophages and their M1 or M2 polarizations have been extensively studied (14), the published data on the phenotype of CXCL4-induced macrophages are scarce. CXCL4 has been shown to induce macrophages expressing CD86, but not HLA-DR on the cell surface (13). We recently showed that CXCL4 strongly suppresses expression of the hemoglobin-haptoglobin receptor CD163 (23). Both findings suggest that the CXCL4 macrophage is distinct from its M-CSF counterpart. However, thus far a comprehensive transcriptome analysis of the CXCL4-induced macrophage phenotype has not been undertaken. Furthermore, it remains unclear whether the CXCL4 macrophage is relevant for atherogenesis and can be related to any of the known polarization patterns.

We hypothesized that the transcriptome of CXCL4-induced macrophages may be unique and different from M-CSF or other known polarization types. Therefore, we conducted a comprehensive analysis of the CXCL4 macrophage transcriptome and compared it to its M-CSF counterpart, speculating that this analysis might give insight into mechanisms by which CXCL4 macrophages promote disease progression in atherosclerosis.

With approval from the institutional review board, PBMCs were isolated from human peripheral blood using Histopaque (Sigma-Aldrich, St.Louis, MO) followed by negative isolation with magnetic beads (Stem Cell, Vancouver, Canada). Monocyte purity was 96.2 ± 0.2% as assessed by CD14 expression. After RBC lysis and several wash steps with 1 mM EDTA, monocytes were essentially free from platelet contamination as demonstrated by virtual absence of CD41 positivity in flow cytometry (data not shown). Monocytes were cultured in macrophage serum-free medium (Life Technologies, Carlsbard, CA) supplemented with Nutridoma SP (Roche, Indianapolis, IN) and penicillin/streptomycin (Sigma-Aldrich) for 6 d in the presence of 100 ng/ml rhM-CSF (Peprotech, Rocky Hill, NJ) or 1 μM rhCXCL4 (Peprotech). The concentration of 1 μM rhCXCL4 was chosen because this concentration was previously demonstrated to be sufficient to induce macrophage differentiation from monocytes (13). Furthermore, our own preliminary experiments confirmed that after 6 d, this concentration induced expression of typical macrophage markers like CD11b or CD68 to a similar extent as M-CSF (Fig. 1 and data not shown).

FIGURE 1.

Primary human monocyte-derived macrophges differentiated with 100 ng/ml M-CSF (M0) or 1 μM CXCL4 (M4). A, Morphology of macrophages after 6 d in culture. Bar indicates 50 μm. B, Gene and protein expression of lineage marker genes PTPCR (CD45), CD14 (CD14), ITGAM (CD11b) in both macrophage types (differences not significant by LPE test). C, The upper graph shows transformed (log2) intensity of all expressed genes in M0 macrophages plotted against the intensity in M4 macrophages (r = 0.934; p < 0.0001). The lower plot shows the same data including only genes with FDR < 0.05 as determined by LPE test. D, Heat map showing all significantly differentially regulated genes (FDR < 0.05, by LPE test). Gene expression was normalized and standardized (gene list in Supplemental Table 1). Red indicates high, and green indicates low gene expression. Genes and conditions were allowed to freely cluster in the y- and x-axes, respectively.

FIGURE 1.

Primary human monocyte-derived macrophges differentiated with 100 ng/ml M-CSF (M0) or 1 μM CXCL4 (M4). A, Morphology of macrophages after 6 d in culture. Bar indicates 50 μm. B, Gene and protein expression of lineage marker genes PTPCR (CD45), CD14 (CD14), ITGAM (CD11b) in both macrophage types (differences not significant by LPE test). C, The upper graph shows transformed (log2) intensity of all expressed genes in M0 macrophages plotted against the intensity in M4 macrophages (r = 0.934; p < 0.0001). The lower plot shows the same data including only genes with FDR < 0.05 as determined by LPE test. D, Heat map showing all significantly differentially regulated genes (FDR < 0.05, by LPE test). Gene expression was normalized and standardized (gene list in Supplemental Table 1). Red indicates high, and green indicates low gene expression. Genes and conditions were allowed to freely cluster in the y- and x-axes, respectively.

Close modal

For foam cell formation assays, macrophages were exposed to 10 μg/ml DiI-labeled acetylated or oxidized low-density lipoprotein (oxLDL; Biomedical Technologies, Stoughton, MA) for 4 h at 37°C. Subsequently, cells were washed and fluorescence intensity was assessed in a flow cytometer (FACScalibur, BD Biosciences, San Jose, CA). Untreated macrophages served as negative controls.

Phagocytosis was assessed using M0 and M4 macrophages as phagocytes and zymosan beads (Alexa Fluor 488; Invitrogen, Carlsbad, CA) as targets, at a ratio of 10 zymosan beads to one macrophage. Macrophages were incubated at 37°C for 1 h with opsonized or nonopsonized zymosan beads. Opsonization was performed by incubation at 37°C for 1 h with autologous serum, followed by three PBS washing steps using low-speed centrifugation (1500 × g, 15 min). The extent of phagocytosis was analyzed by flow cytometry, using untreated macrophages (no beads) as controls.

For each condition RNA was isolated from macrophages derived from two donors using columns including a DNAse-step followed by reverse transcription (all reagents from Qiagen, Valencia, CA). RNA was labeled and hybridized to Affymetrix (Santa Clara, CA) HG133 Plus 2.0 arrays as described previously (24). For each donor, RNA was hybridized to a separate gene array. Signal intensity values were obtained from the Affymetrix MicroArray Suite software (MAS 5.0). The data set and technical information according to the Minimum Information about a Microarray Experiment requirements are available at the Gene Expression Omnibus Web site (www.ncbi.nlm.nih.gov/geo), accession number GSE20484.

Protein concentration of selected cytokines was measured in cell culture supernatants using ELISA (CCL18 [R&D Systems, Minneapolis, MN] and CCL22 [Cell Sciences, Canton, MA]) or cytokine bead arrays (IL-1β, IL-6, IL-8, IL-10, IL-12p70, TNF; BD Biosciences) according to the manufacturers’ instructions. Supernatants were pooled over 6 d and diluted when necessary to obtain concentrations within the range of the assays.

For flow cytometry, cells were treated with Fc block (Miltenyi Biotec, Auburn, CA) and subsequently stained with antibodies against CD36 (clone CB38; BD Biosciences) and SR-A (clone 351615; R&D Systems). For SR-A staining, an FITC-labeled secondary Ab was used. Appropriate isotype controls were used in all experiments. Fluorescence was measured on a FACSCalibur flow cytometer (BD Biosciences). Fluorescence was assessed as background-corrected mean fluorescence.

For statistical analysis, the open source statistical software package R (www.r-project.org) was used including the local pooled error (LPE) test for differential expression discovery between two conditions (25). Gene chip data were analyzed as described previously (24). After exclusion of nonexpressed genes, data were normalized and log2 transformed to achieve normal distribution. The LPE test is statistically powerful for identifying differentially expressed genes between low-replicated microarray data. It pools probe sets with similar expression levels providing a statistic for each probe set. The absolute value of the LPE-statistic is larger for more significantly differentially expressed probe sets. A false discovery rate (FDR) was calculated to discover probe sets differentially expressed with FDR < 0.05 (26). Heat maps were constructed using R in a way that allows all conditions and genes to freely cluster both in the x- (condition) and the y-axes (gene).

To assess functional networks regulated in each macrophage type, profiled complex functionality was analyzed using the ProfCom software (National Institutes of Health), a Web-based tool for the interpretation of genes that were identified to be functionally linked by experiment (27). This tool corrects for multiple testing and compares the proportion of genes related to specific gene ontology (GO) categories among the genes found regulated to the proportion of genes related to the same category within the gene ontology reference genes.

Gene set enrichment was analyzed using an open access software for gene set enrichment analysis (GSEA) (28) to assess potential similarities between the CXCL4-induced gene expression profile and the known M1 and M2 signatures. The latter were extracted from the gene expression data of monocyte–macrophage differentiation and polarization as published by Mantovani et al. (14) (GEO data set 2430). Using the LPE test, genes differentially expressed between the M1 and M2 data set were identified and used as M1 and M2 gene sets, respectively. Overexpression of the M1 and M2 gene sets was tested by GSEA in the M-CSF and CXCL4 gene expression data. GSEA calculates an enrichment score, which indicates the degree of overrepresentation of these gene sets and estimates its significance with adjustment for multiple hypothesis testing.

A modified principal component analysis (PCA) was performed on the previously published M1 and M2 (14) as well as on the new CXCL4 gene expression data normalized to the corresponding M-CSF gene expression sets. This normalization step avoided bias owing to interexperimental variance. First, PCA was performed including all genes that were significantly overexpressed (as determined by LPE) in M1 relative to M2. Subsequently, a second PCA was performed including all genes that were overexpressed in M2 relative to M1. The first principal components from each of these analyses (independent by definition) were used to define a new coordinate space in which CXCL4 gene expression data were plotted.

Hierarchical clustering was used to determine the level of similarity between the three normalized groups (29). All genes were included in the analysis, and the results are displayed in a dendrogram. Distance was determined by average linkage, wherein the distance between two groups, A and B, is determined according to the equation

dAB=1nAnBiAjBdij,
(1)

where ni is the number of members in group i and dij is the Euclidean distance between two points, i and j.

Preliminary experiments confirmed that after 6 d in culture both M-CSF– as well as CXCL4-induced macrophages (which we suggest calling M0 and M4, respectively) displayed a morphology characteristic of macrophages (Fig. 1A). Accordingly, gene and protein expression of the classical lineage markers (CD45, CD14, and CD11b) were comparable in both macrophage types, indicating that the cells studied were fully differentiated macrophages (Fig. 1B).

When comparing the overall gene expression signature of M0 and M4 macrophages, the two gene expression patterns were found to be similar and highly correlated (r = 0.934; p < 0.0001; Fig. 1C, upper panel). Of 26,051 probe sets expressed above the detection limit in at least one macrophage sample, 460 annotated probe sets were significantly upregulated or downregulated with FDR < 0.05 corresponding to a total number of 375 regulated genes (Fig. 1C, lower panel). Two hundred six of these genes displayed higher and 169 displayed lower expression levels in M4 macrophages as compared with M0 macrophages (Fig. 1D). A list of differentially expressed genes is given in Supplemental Table I.

Based on the genes found to be differentially expressed between M0 and M4 macrophages by LPE test, we sought to identify functional processes as defined by gene ontology that were transcriptionally overrepresented in M4 macrophages. Applying profiling of complex functionality analysis (27), we found a number of biologic processes that were associated with genes expressed in M0 or M4 macrophages (Fig. 2A–F). Most prominently, both M0 and M4 macrophages overexpressed genes related to the inflammatory and the immune response. In M4 macrophages, CCL18 and TNFSF10 (TRAIL) were overexpressed, whereas in M0 macrophages AIF1, ALOX5, and IL1RN were found in the inflammation and immune response gene sets (in all cases, p < 0.05).

FIGURE 2.

GO categories of regulated genes in M0 and M4 macrophages as determined by ProfCom analysis. Bars indicate the percentage (A, C, E) or the absolute number (B, D, F) of genes attributed to a certain GO category within all genes of the GO data set (empty bars), genes overexpressed in M0 macrophages (black bars), or genes overexpressed in M4 macrophages (gray bars). Data are arranged by biologic process (A, B), cellular component (C, D), and molecular function (E, F). *p < 0.05, adjusted for multiple testing.

FIGURE 2.

GO categories of regulated genes in M0 and M4 macrophages as determined by ProfCom analysis. Bars indicate the percentage (A, C, E) or the absolute number (B, D, F) of genes attributed to a certain GO category within all genes of the GO data set (empty bars), genes overexpressed in M0 macrophages (black bars), or genes overexpressed in M4 macrophages (gray bars). Data are arranged by biologic process (A, B), cellular component (C, D), and molecular function (E, F). *p < 0.05, adjusted for multiple testing.

Close modal

Genes involved in Ag processing and presentation were significantly overrepresented in M4 macrophages (p < 0.05), including HLA-DRB1, HLA-DRB3, HLA-DRB4, and HLA-DQA1 (coding for MHC class II), as well as the costimulatory surface molecule CD86. Interestingly, several genes implicated in lipid metabolism and transport were also found overexpressed (p < 0.05), including APOC2, APOE, and SORL1. By contrast, genes overexpressed in M-CSF–induced macrophages were more likely to be implicated in chemotaxis (represented by the chemokines CCL3, CCL7, and the chemokine receptor CCR1; p < 0.05) or cell adhesion, as indicated by the integrin genes ITGAV, ITGA6, ITGB8B, and the COL6A gene coding for the extracellular matrix component collagen 6A; p < 0.05.

As reported previously, M0 macrophages display a gene expression pattern similar to that of M2 macrophages, whereas the gene signature of M1 macrophages is distinct (14). To better understand the characteristics of M4 macrophages, we sought to assess whether the M4 macrophage transcriptome is comparable to either of these polarization types. When examining selected genes’ characteristics for M1 or M2 polarization, it became clear that a large number of polarization marker genes were not differentially expressed between M0 and M4 macrophages. This finding was true for a number of cytokines (IL6, IL12, TNF [TNF-α, all M1], IL10 [M2]), many chemokines (CCL2, CCL5 [both M1], CCL1 [M2]), several surface receptors (CCR7, TLR2, TLR4 [all M1]), or specific enzymes (NOS2 [iNOS, M1], ARG1 [arginase-1, M2]) (8). Alternatively, a small number of marker genes displayed significant differential expression between M0 and M4 macrophages; however, there was no clear pattern for preferential expression of M1 or M2 markers in either of the macrophage types (Fig. 3A). Table I shows all M1 and M2 genes significantly overexpressed in either M0 or M4 macrophages. Measuring protein levels of cytokines released into cell culture supernatants largely confirmed this pattern with IL-6, TNF (both M1), CCL18, and CCL22 (both M2) levels being higher in M4 macrophages, and IL-10 (M2) levels being higher in M0 macrophages (Fig. 3B). No differences were seen for the levels of IL-1β, IL-8, and IL-12p70 (data not shown).

FIGURE 3.

M4 macrophages do not display a clear M1 or M2 transcriptome pattern. A, Gene expression of selected markers for M1 and M2 polarization (TNFSF10, TRAIL; PTX3, pentraxin-3; MRC1, mannose receptor). ***FDR < 0.001, by LPE test. B, Gene expression (upper row) and protein levels (bottom row) of cytokines related to M1 or M2 polarization. ***FDR < 0.001, by LPE test (gene expression); *p < 0.05 by paired t test (protein levels), dotted line indicates detection limit of the assay. C, Enrichment plot of M1 or M2 gene sets in M0 versus M4 macrophages. All genes were ranked using the GSEA difference of class metric. For each gene in the M1 or M2 gene set, the enrichment score was calculated and plotted against the position of the genes within the rank ordered data set. In both cases, no significant enrichment was found.

FIGURE 3.

M4 macrophages do not display a clear M1 or M2 transcriptome pattern. A, Gene expression of selected markers for M1 and M2 polarization (TNFSF10, TRAIL; PTX3, pentraxin-3; MRC1, mannose receptor). ***FDR < 0.001, by LPE test. B, Gene expression (upper row) and protein levels (bottom row) of cytokines related to M1 or M2 polarization. ***FDR < 0.001, by LPE test (gene expression); *p < 0.05 by paired t test (protein levels), dotted line indicates detection limit of the assay. C, Enrichment plot of M1 or M2 gene sets in M0 versus M4 macrophages. All genes were ranked using the GSEA difference of class metric. For each gene in the M1 or M2 gene set, the enrichment score was calculated and plotted against the position of the genes within the rank ordered data set. In both cases, no significant enrichment was found.

Close modal
Table I.
Probe sets and genes attributed to M1 polarization or M2 polarization (22) with significantly differential expression between M0 and M4 macrophages, according to LPE test: log2 normalized expression data, z stats, and FDR as calculated by LPE test
GeneM0M0M4M4
Probe SetSymbolAnnotation#1#2#1#2z StatsFDR
Probe sets/genes related to M1 polarization  
 205686_s_at CD86 CD86 9.72 10.07 11.66 11.57 −6.97 <0.0001 
 210895_s_at CD86 CD86 11.27 11.66 12.89 13.01 −6.74 <0.0001 
 209728_at HLA-DRB4 MHC, class II, DR β 4 11.86 11.57 12.90 13.29 −6.28 0.0017 
 205685_at CD86 CD86 8.93 9.34 10.64 10.82 −5.84 0.0039 
 213831_at HLA-DQA1 MHC, class II, DQ α 1 9.86 2.03 10.77 5.92 −5.62 0.0075 
 204670_x_at HLA-DRB1 MHC, class II, DR β 1 12.41 11.94 13.09 13.58 −5.22 0.0105 
 204972_at OAS2 2′-5′-oligoadenylate synthetase 2 8.14 7.64 9.23 9.52 −4.72 0.0188 
 202688_at TNFSF10 TNF-related apoptosis inducing ligand TRAIL 6.12 5.83 7.83 8.12 −4.63 0.0202 
 208306_x_at HLA-DRB1 MHC, class II, DR β 1 12.66 12.65 13.34 13.98 −4.39 0.0284 
 215193_x_at HLA-DRB1 MHC, class II, DR β 1 12.45 12.16 13.14 13.38 −4.37 0.0298 
 206157_at PTX3 Pentraxin 3 10.76 10.85 9.65 9.69 4.32 0.0322 
Probe sets/genes related to M2 polarization  
 207861_at CCL22 CCL22 11.32 11.14 14.40 15.28 −11.47 <0.0001 
 32128_at CCL18 Pulmonary and activation-regulated chemokine (PARC) 5.34 4.84 9.29 9.94 −7.92 <0.0001 
 204438_at MRC1 Mannose receptor 10.64 10.67 11.99 12.58 −7.13 <0.0001 
 223280_x_at MS4A6A Membrane-spanning 4-domains, subfamily A, member 6A 7.55 5.53 9.68 8.58 −7.02 <0.0001 
 224356_x_at MS4A6A Membrane-spanning 4-domains, subfamily A, member 6A 7.78 5.93 9.80 8.81 −6.98 <0.0001 
 204112_s_at HNMT Histamine N-methyltransferase 12.05 12.45 10.51 10.77 6.84 <0.0001 
 201427_s_at SEPP1 Selenoprotein P, plasma, 1 10.16 9.03 8.33 3.89 7.64 <0.0001 
 209555_s_at CD36 CD36 14.32 14.50 13.02 12.54 9.08 <0.0001 
 206488_s_at CD36 CD36 14.37 14.47 13.13 12.42 9.15 <0.0001 
 211719_x_at FN1 Fibronectin 1 11.35 12.01 9.45 6.72 12.59 <0.0001 
 212464_s_at FN1 Fibronectin 1 11.62 11.88 10.06 6.25 12.69 <0.0001 
 210495_x_at FN1 Fibronectin 1 11.56 12.13 9.63 6.72 12.99 <0.0001 
 216442_x_at FN1 Fibronectin 1 11.42 12.01 9.46 4.17 14.06 <0.0001 
 209924_at CCL18 Pulmonary and activation-regulated chemokine 6.25 5.71 7.93 9.33 −6.31 0.0017 
 219666_at MS4A6A Membrane-spanning 4-domains, subfamily A, member 6A 7.14 6.29 9.31 8.30 −5.73 0.0039 
 208422_at MSR1 Scavenger receptor-A 7.93 12.27 5.76 11.11 5.72 0.0039 
 227265_at FGL2 Fibrinogen-like protein 2 8.66 7.25 10.55 8.80 −5.55 0.0075 
 204834_at FGL2 Fibrinogen-like protein 2 8.50 7.86 10.42 9.26 −5.49 0.0075 
 211732_x_at HNMT Histamine N-methyltransferase 10.11 10.63 8.79 9.51 4.44 0.0259 
 228772_at HNMT Histamine N-methyltransferase 8.42 8.81 7.07 7.33 4.00 0.0463 
GeneM0M0M4M4
Probe SetSymbolAnnotation#1#2#1#2z StatsFDR
Probe sets/genes related to M1 polarization  
 205686_s_at CD86 CD86 9.72 10.07 11.66 11.57 −6.97 <0.0001 
 210895_s_at CD86 CD86 11.27 11.66 12.89 13.01 −6.74 <0.0001 
 209728_at HLA-DRB4 MHC, class II, DR β 4 11.86 11.57 12.90 13.29 −6.28 0.0017 
 205685_at CD86 CD86 8.93 9.34 10.64 10.82 −5.84 0.0039 
 213831_at HLA-DQA1 MHC, class II, DQ α 1 9.86 2.03 10.77 5.92 −5.62 0.0075 
 204670_x_at HLA-DRB1 MHC, class II, DR β 1 12.41 11.94 13.09 13.58 −5.22 0.0105 
 204972_at OAS2 2′-5′-oligoadenylate synthetase 2 8.14 7.64 9.23 9.52 −4.72 0.0188 
 202688_at TNFSF10 TNF-related apoptosis inducing ligand TRAIL 6.12 5.83 7.83 8.12 −4.63 0.0202 
 208306_x_at HLA-DRB1 MHC, class II, DR β 1 12.66 12.65 13.34 13.98 −4.39 0.0284 
 215193_x_at HLA-DRB1 MHC, class II, DR β 1 12.45 12.16 13.14 13.38 −4.37 0.0298 
 206157_at PTX3 Pentraxin 3 10.76 10.85 9.65 9.69 4.32 0.0322 
Probe sets/genes related to M2 polarization  
 207861_at CCL22 CCL22 11.32 11.14 14.40 15.28 −11.47 <0.0001 
 32128_at CCL18 Pulmonary and activation-regulated chemokine (PARC) 5.34 4.84 9.29 9.94 −7.92 <0.0001 
 204438_at MRC1 Mannose receptor 10.64 10.67 11.99 12.58 −7.13 <0.0001 
 223280_x_at MS4A6A Membrane-spanning 4-domains, subfamily A, member 6A 7.55 5.53 9.68 8.58 −7.02 <0.0001 
 224356_x_at MS4A6A Membrane-spanning 4-domains, subfamily A, member 6A 7.78 5.93 9.80 8.81 −6.98 <0.0001 
 204112_s_at HNMT Histamine N-methyltransferase 12.05 12.45 10.51 10.77 6.84 <0.0001 
 201427_s_at SEPP1 Selenoprotein P, plasma, 1 10.16 9.03 8.33 3.89 7.64 <0.0001 
 209555_s_at CD36 CD36 14.32 14.50 13.02 12.54 9.08 <0.0001 
 206488_s_at CD36 CD36 14.37 14.47 13.13 12.42 9.15 <0.0001 
 211719_x_at FN1 Fibronectin 1 11.35 12.01 9.45 6.72 12.59 <0.0001 
 212464_s_at FN1 Fibronectin 1 11.62 11.88 10.06 6.25 12.69 <0.0001 
 210495_x_at FN1 Fibronectin 1 11.56 12.13 9.63 6.72 12.99 <0.0001 
 216442_x_at FN1 Fibronectin 1 11.42 12.01 9.46 4.17 14.06 <0.0001 
 209924_at CCL18 Pulmonary and activation-regulated chemokine 6.25 5.71 7.93 9.33 −6.31 0.0017 
 219666_at MS4A6A Membrane-spanning 4-domains, subfamily A, member 6A 7.14 6.29 9.31 8.30 −5.73 0.0039 
 208422_at MSR1 Scavenger receptor-A 7.93 12.27 5.76 11.11 5.72 0.0039 
 227265_at FGL2 Fibrinogen-like protein 2 8.66 7.25 10.55 8.80 −5.55 0.0075 
 204834_at FGL2 Fibrinogen-like protein 2 8.50 7.86 10.42 9.26 −5.49 0.0075 
 211732_x_at HNMT Histamine N-methyltransferase 10.11 10.63 8.79 9.51 4.44 0.0259 
 228772_at HNMT Histamine N-methyltransferase 8.42 8.81 7.07 7.33 4.00 0.0463 

To generate larger gene sets for M1 or M2 polarization, we compared gene array data sets for M1 and M2 polarized macrophages as published by Mantovani et al. (14). These gene expression data were derived from human monocyte-derived M-CSF macrophages, which were treated with either LPS and IFN-γ (M1) or IL-4 (M2) (14). Genes with FDR < 0.05, as determined by LPE testing between M1 and M2 (Supplemental Table 2), were included in the gene sets (Supplemental Table 3). Using these gene sets, we performed GSEA for M1 and M2 genes. This demonstrated no significant overexpression of either of the gene sets in M0 or M4 macrophages (FDR = 0.98 [M1 gene set] and 1.0 [M2 gene set], respectively; Fig. 3C). This finding establishes that M4 macrophages are neither M1 nor M2 but represent a distinct phenotype.

To test whether the M4 macrophage transcriptome is similar to any of the classical polarization patterns (M1 and M2), we used a modified PCA on the normalized M1 and M2 data (14). At first, PCA was performed including all genes that were significantly overexpressed (as determined by LPE) in M1 relative to M2 (n = 2431). A second PCA was performed that included all genes overexpressed in M2 relative to M1 (n = 3944). Based on the principal components from each of these analyses, a new coordinate space was defined in which the M4 gene expression data were plotted. The M4 macrophages did not cluster with either M1 or M2 macrophages (Fig. 4A). To corroborate this finding, we used hierarchical clustering, including all genes of the M1, M2, and M4 macrophage expression data. This analysis confirmed that the M4 transcriptome significantly differs from M1 or M2 macrophages and represents a unique macrophage phenotype. In fact, M1 and M2 are more similar to each other than to M4 (Fig. 4B).

FIGURE 4.

The M4 macrophage transcriptome is distinct from the M1 or M2 transcriptomes. A, Modified principal components analysis of M1 and M2 gene expression data (as described in 1Materials and Methods). M4 gene expression data were plotted into a coordinate space defined by M1 and M2 gene expression data. B, Hierarchical clustering of the normalized M1, M2, and M4 gene expression data. All genes were included in the analysis, and the results are displayed as dendrogram. #1, #2, and #3 indicate donor-specific replicates for each condition.

FIGURE 4.

The M4 macrophage transcriptome is distinct from the M1 or M2 transcriptomes. A, Modified principal components analysis of M1 and M2 gene expression data (as described in 1Materials and Methods). M4 gene expression data were plotted into a coordinate space defined by M1 and M2 gene expression data. B, Hierarchical clustering of the normalized M1, M2, and M4 gene expression data. All genes were included in the analysis, and the results are displayed as dendrogram. #1, #2, and #3 indicate donor-specific replicates for each condition.

Close modal

One function of macrophages is to phagocytose pathogens and foreign materials (29). Phagocytosis was recently shown to be inhibited in M2-polarized macrophages (30). To test the phagocytosis function, we incubated M0 and M4 macrophages with zymosan beads with and without serum opsonization. Although ∼20% of M0 macrophages phagocytosed zymosan beads, this function was almost completely suppressed in M4 macrophages (Fig. 5A, 5B).

FIGURE 5.

M0 and M4 macrophages display differential phagocytotic capacity. Macrophages differentiated with M-CSF (M0) or CXCL4 (M4) were exposed to zymosan beads (A) or zymosan beads opsonized with FCS (B) as described in 1Materials and Methods. The phagocytotic capacity of M0 and M4 macrophages was assessed by flow cytometry. Representative histograms are shown in A and B, and results of two independent experiments are presented as bar graphs in C. **p < 0.01.

FIGURE 5.

M0 and M4 macrophages display differential phagocytotic capacity. Macrophages differentiated with M-CSF (M0) or CXCL4 (M4) were exposed to zymosan beads (A) or zymosan beads opsonized with FCS (B) as described in 1Materials and Methods. The phagocytotic capacity of M0 and M4 macrophages was assessed by flow cytometry. Representative histograms are shown in A and B, and results of two independent experiments are presented as bar graphs in C. **p < 0.01.

Close modal

To understand the potential relevance of CXCL4-induced macrophages in atherogenesis, we further investigated the list of differentially expressed genes. As indicated by GO analysis, these included several chemokines and matrix metalloproteases and two members of the cathepsin family, but also some genes implicated in lipid metabolism and foam cell formation. Most of these gene groups did not display a consistent pattern, indicating that both proatherogenic and anti-atherogenic genes were expressed in M4 macrophages. Whereas MMP7 and MMP12 showed higher expression in M4 than in M0 macrophages, MMP8 expression was higher in M0 macrophages (Fig. 6A).

FIGURE 6.

Atherosclerosis-related genes in M4 macrophages and potential functional implications. A, Expression of atherosclerosis-related genes with significantly different expression level in M0 and M4 macrophages. *FDR < 0.01; ***FDR < 0.001. B, Heat map of genes implicated in foam cell formation. Red indicates high gene expression, and green indicates low gene expression. Genes and conditions were allowed to cluster freely in the y- and x-axes, respectively. C, Representative histograms of surface expression of scavenger receptor-A (SR-A) and CD36 in M0 (solid line) and M4 (dotted line) macrophages derived from the same donor. Isotype control shown in gray. D and E, Mean fluorescence intensity of DiI-labeled acetylated (D) or oxidized (E) LDL in M0 (solid line) and M4 (dotted line) macrophages after 4 h exposure to 10 μg/ml LDL, as determined by flow cytometry. Representative histograms and a bar graph summarizing the flow cytometric data are shown. *p < 0.05 by t test, mean ± SEM; n = 3–6.

FIGURE 6.

Atherosclerosis-related genes in M4 macrophages and potential functional implications. A, Expression of atherosclerosis-related genes with significantly different expression level in M0 and M4 macrophages. *FDR < 0.01; ***FDR < 0.001. B, Heat map of genes implicated in foam cell formation. Red indicates high gene expression, and green indicates low gene expression. Genes and conditions were allowed to cluster freely in the y- and x-axes, respectively. C, Representative histograms of surface expression of scavenger receptor-A (SR-A) and CD36 in M0 (solid line) and M4 (dotted line) macrophages derived from the same donor. Isotype control shown in gray. D and E, Mean fluorescence intensity of DiI-labeled acetylated (D) or oxidized (E) LDL in M0 (solid line) and M4 (dotted line) macrophages after 4 h exposure to 10 μg/ml LDL, as determined by flow cytometry. Representative histograms and a bar graph summarizing the flow cytometric data are shown. *p < 0.05 by t test, mean ± SEM; n = 3–6.

Close modal

Strikingly, genes implicated in foam cell formation (i.e., the scavenger receptors CD36 and MSR1 [SR-A] and the cholesterol efflux transporter ABCG1) displayed highly differential expression between M0 and M4 macrophages (ABCA1 mRNA was expressed in neither M0 nor M4 macrophages). Although M0 macrophages expressed higher levels of CD36 and MSR1 mRNA, ABCG1 mRNA expression was higher in M4 macrophages. This gene expression pattern suggested that M4 macrophage would be less likely to take up modified LDL and more likely to promote cholesterol efflux (Fig. 6A, 6B). At the protein level, CD36 expression was slightly lower in M4 macrophages, whereas no significant difference was seen for SR-A expression (Fig. 6C).To assess the functional relevance of this finding, we studied uptake of DiI-labeled acetylated LDL (acLDL) or oxLDL by M0 and M4 macrophages. After 4 h exposure to 10 μg/ml DiI-labeled acLDL or oxLDL, M4 macrophages displayed a significantly reduced content of modified LDL as assessed by flow cytometry (Fig. 6D, 6E). This finding suggests that the gene signature actually translates into cellular function and that the prevailing gene expression pattern of M4 macrophages tends to result in reduced foam cell formation.

We report the first comprehensive analysis of the transcriptome of monocyte-derived macrophages induced by the chemokine CXCL4. Our study demonstrates that (1) CXCL4 induces a macrophage phenotype that is distinct from that induced by M-CSF (2), the CXCL4-induced transcriptome shares similarities with, but is also distinct from, each of the classical M1 and M2 phenotypes, and (3) the transcriptome of CXCL4-induced macrophages is not clearly pro- or anti-atherogenic. Based on its unique properties, we suggest referring to the macrophage polarization induced by CXCL4 as M4 macrophages.

Our knowledge about heterogeneity of polarized macrophages has increased significantly. Thus, in addition to the classical M1 macrophages (characterized by high expression of proinflammatory cytokines, iNOS expression, and production of reactive oxygen species) and M2 macrophages (expressing high levels of mannose receptor, dectin-1, and arginase), a number of M2 subsets have been characterized (1). These subsets include M2 macrophages activated by IL-4 or IL-13 (now termed M2a), macrophages activated by immune complexes (termed M2b), and macrophages polarized with glucocorticoids or IL-10 (M2c) (31). The M-CSF–induced transcriptome and the corresponding M1 and M2 (more specifically, M2a) transcriptomes have been studied by Mantovani et al. using Affymetrix gene chips (14). These experiments showed a close similarity between M-CSF–induced and M2a macrophages. Furthermore, they demonstrated differences between M1 and M2a in genes involved in metabolic activities as well as genes coding for chemokines (14).

Similar to M-CSF, the platelet chemokine CXCL4 has been demonstrated to prevent monocyte apoptosis and promote macrophage differentiation from human peripheral blood monocytes (13). Surprisingly, the phenotype of these CXCL4-induced macrophages has not been studied in detail. Our data suggest that CXCL4 induces a macrophage phenotype that shares similarities with both M1 and M2 macrophages. Thus, some M1- and M2-related genes are overexpressed in M4 macrophages as compared with M-CSF–induced macrophages. This finding was confirmed for a number of cytokines on the protein level. Most importantly, an unbiased analysis using different approaches such as gene set enrichment, modified principal component, and hierarchical clustering analysis all confirmed the uniqueness of the CXCL4-induced macrophage transcriptome.

Platelets, as well as monocytes and monocyte-derived macrophages, are present within atherosclerotic lesions, and it is now clear that both contribute to lesion formation (5). The platelet chemokine CXCL4 is known to promote atherosclerosis as demonstrated in CXCL4-deficient PF4−/− mice. On the Apoe−/− background, the PF4−/− mice showed ∼60% reduction of lesion size in the aorta (32). One way by which CXCL4 may contribute to atherogenesis is by promoting macrophage differentiation from monocytes present in the arterial wall. It had been speculated that CXCL4 could induce a macrophage polarization favorable for the development of atherosclerotic lesions. Our in vitro data suggest that CXCL4 alone is not sufficient to promote atherosclerosis, because compared with M-CSF–induced M0 macrophages, CXCL4-induced M4 macrophages express a number of atherosclerosis-related genes at higher and others at lower levels.

Thus, compared with M0 macrophages, CXCL4 induced high expression of the matrix metalloproteases 7 and 12, whereas MMP-8 was expressed only at low levels. Although all three MMPs have been clearly implicated in atherosclerosis (33), the gene expression data need to be interpreted with caution, because the activity of MMPs is regulated by complex mechanisms involving proteolytic cleavage by cathepsins, MMPs, and serine proteases (33). Accordingly, changes in gene expression do not necessarily indicate changes in activity in vivo (33). Although genes coding for the apolipoproteins APOC2 and APOE were expressed only at low levels in CXCL4 macrophages, two members of the proteolytic cathepsin family (B and K) showed high gene expression levels. Several cathepsins have been found to be overexpressed in atherosclerotic lesions and contribute to atherogenesis through different mechanisms, including effects on lipid metabolism, inflammation, and MMP activity (34).

Most strikingly, when examining expression levels of genes implicated in foam cell formation, CXCL4 macrophages showed low levels of scavenger receptors necessary for uptake of modified LDL and at the same time higher levels of the cholesterol efflux transporter ABCG1. The finding that exposure of M4 macrophages to acLDL or oxLDL resulted in less intracellular cholesterol content than in M0 macrophages from the same donor suggests that these findings on the gene expression level translate into relevant functional differences.

In vivo, entire platelets with their granule contents are present in atherosclerotic lesions and not isolated CXCL4. Thus, CXCL4 can synergize with other platelet elements to induce a proatherosclerotic macrophage phenotype that is believed to be lacking in PF4−/− mice. In fact, it has been demonstrated in Apoe−/− mice that pharmacologic inhibition of heterodimerization of CXCL4 with CCL5, which is also released from activated platelets, resulted in significant reduction of lesion formation (35). We recently showed that CXCL4-induced macrophages lack expression of the hemoglobin scavenger receptor CD163. CXCL4 and CD163 expression are inversely correlated in human atherosclerotic lesions (23). This finding supports the notion that the M4 macrophage phenotype can actually be identified within human atherosclerotic lesions and might have pathophysiologic relevance in atherosclerosis.

Our data provide new insight into the process of macrophage differentiation. By comparing the transcriptome of M-CSF– and CXCL4-induced macrophages in vitro, we identify M4 macrophages and provide novel starting points for further atherosclerosis- and other disease-related research.

We thank Keely Arbenz-Smith for technical assistance.

Disclosures The authors have no financial conflicts of interest.

This work was supported by Grant GL599/1-1 from the Deutsche Forschungsgemeinschaft (to C.A.G.) and National Institutes of Health Grant 58108 (to K.L.).

The online version of this article contains supplemental material.

Abbreviations used in this paper:

acLDL

acetylated LDL

FDR

false discovery rate

GO

gene ontology

GSEA

gene set enrichment analysis

LDL

low-density lipoprotein

LPE

local pooled error

oxLDL

oxidized LDL

PCA

principal compenent analysis

SR-A

scavenger receptor-A.

1
Gordon
S.
,
Taylor
P. R.
.
2005
.
Monocyte and macrophage heterogeneity.
Nat. Rev. Immunol.
5
:
953
964
.
2
Gerrity
R. G.
1981
.
The role of the monocyte in atherogenesis: II. Migration of foam cells from atherosclerotic lesions.
Am. J. Pathol.
103
:
191
200
.
3
Gerrity
R. G.
1981
.
The role of the monocyte in atherogenesis: I. Transition of blood-borne monocytes into foam cells in fatty lesions.
Am. J. Pathol.
103
:
181
190
.
4
Ross
R.
1993
.
The pathogenesis of atherosclerosis: a perspective for the 1990s.
Nature
362
:
801
809
.
5
Galkina
E. V.
,
Ley
K.
.
2009
.
Immune and inflammatory mechanisms of atherosclerosis.
Annu. Rev. Immunol.
27
:
165
197
.
6
Gordon
S.
2007
.
Macrophage heterogeneity and tissue lipids.
J. Clin. Invest.
117
:
89
93
.
7
Varin
A.
,
Gordon
S.
.
2009
.
Alternative activation of macrophages: Immune function and cellular biology.
Immunobiology
[Epub ahead of print]
.
8
Mantovani
A.
,
Sica
A.
,
Sozzani
S.
,
Allavena
P.
,
Vecchi
A.
,
Locati
M.
.
2004
.
The chemokine system in diverse forms of macrophage activation and polarization.
Trends Immunol.
25
:
677
686
.
9
Bouhlel
M. A.
,
Derudas
B.
,
Rigamonti
E.
,
Dièvart
R.
,
Brozek
J.
,
Haulon
S.
,
Zawadzki
C.
,
Jude
B.
,
Torpier
G.
,
Marx
N.
, et al
.
2007
.
PPARgamma activation primes human monocytes into alternative M2 macrophages with anti-inflammatory properties.
Cell Metab.
6
:
137
143
.
10
Waldo
S. W.
,
Li
Y.
,
Buono
C.
,
Zhao
B.
,
Billings
E. M.
,
Chang
J.
,
Kruth
H. S.
.
2008
.
Heterogeneity of human macrophages in culture and in atherosclerotic plaques.
Am. J. Pathol.
172
:
1112
1126
.
11
Boyle
J. J.
,
Harrington
H. A.
,
Piper
E.
,
Elderfield
K.
,
Stark
J.
,
Landis
R. C.
,
Haskard
D. O.
.
2009
.
Coronary intraplaque hemorrhage evokes a novel atheroprotective macrophage phenotype.
Am. J. Pathol.
174
:
1097
1108
.
12
Metcalf
D.
1986
.
The molecular biology and functions of the granulocyte-macrophage colony-stimulating factors.
Blood
67
:
257
267
.
13
Scheuerer
B.
,
Ernst
M.
,
Dürrbaum-Landmann
I.
,
Fleischer
J.
,
Grage-Griebenow
E.
,
Brandt
E.
,
Flad
H. D.
,
Petersen
F.
.
2000
.
The CXC-chemokine platelet factor 4 promotes monocyte survival and induces monocyte differentiation into macrophages.
Blood
95
:
1158
1166
.
14
Martinez
F. O.
,
Gordon
S.
,
Locati
M.
,
Mantovani
A.
.
2006
.
Transcriptional profiling of the human monocyte-to-macrophage differentiation and polarization: new molecules and patterns of gene expression.
J. Immunol.
177
:
7303
7311
.
15
Yoshida
H.
,
Hayashi
S.
,
Kunisada
T.
,
Ogawa
M.
,
Nishikawa
S.
,
Okamura
H.
,
Sudo
T.
,
Shultz
L. D.
,
Nishikawa
S.
.
1990
.
The murine mutation osteopetrosis is in the coding region of the macrophage colony stimulating factor gene.
Nature
345
:
442
444
.
16
de Villiers
W. J.
,
Smith
J. D.
,
Miyata
M.
,
Dansky
H. M.
,
Darley
E.
,
Gordon
S.
.
1998
.
Macrophage phenotype in mice deficient in both macrophage-colony-stimulating factor (op) and apolipoprotein E.
Arterioscler. Thromb. Vasc. Biol.
18
:
631
640
.
17
Gewirtz
A. M.
,
Calabretta
B.
,
Rucinski
B.
,
Niewiarowski
S.
,
Xu
W. Y.
.
1989
.
Inhibition of human megakaryocytopoiesis in vitro by platelet factor 4 (PF4) and a synthetic COOH-terminal PF4 peptide.
J. Clin. Invest.
83
:
1477
1486
.
18
Gleissner
C. A.
,
Ley
K.
.
2007
.
CXCL4 in atherosclerosis: possible roles in monocyte arrest and macrophage foam cell formation.
Thromb. Haemost.
98
:
917
918
.
19
Kasper
B.
,
Brandt
E.
,
Brandau
S.
,
Petersen
F.
.
2007
.
Platelet factor 4 (CXC chemokine ligand 4) differentially regulates respiratory burst, survival, and cytokine expression of human monocytes by using distinct signaling pathways.
J. Immunol.
179
:
2584
2591
.
20
Gleissner
C. A.
,
von Hundelshausen
P.
,
Ley
K.
.
2008
.
Platelet chemokines in vascular disease.
Arterioscler. Thromb. Vasc. Biol.
28
:
1920
1927
.
21
Pitsilos
S.
,
Hunt
J.
,
Mohler
E. R.
,
Prabhakar
A. M.
,
Poncz
M.
,
Dawicki
J.
,
Khalapyan
T. Z.
,
Wolfe
M. L.
,
Fairman
R.
,
Mitchell
M.
, et al
.
2003
.
Platelet factor 4 localization in carotid atherosclerotic plaques: correlation with clinical parameters.
Thromb. Haemost.
90
:
1112
1120
.
22
Sachais
B. S.
,
Turrentine
T.
,
Dawicki McKenna
J. M.
,
Rux
A. H.
,
Rader
D.
,
Kowalska
M. A.
.
2007
.
Elimination of platelet factor 4 (PF4) from platelets reduces atherosclerosis in C57Bl/6 and apoE-/- mice.
Thromb. Haemost.
98
:
1108
1113
.
23
Gleissner
C. A.
,
Shaked
I.
,
Erbel
C.
,
Böckler
D.
,
Katus
H. A.
,
Ley
K.
.
2010
.
CXCL4 downregulates the atheroprotective hemoglobin receptor CD163 in human macrophages.
Circ. Res.
106
:
203
211
.
24
Cho
H. J.
,
Shashkin
P.
,
Gleissner
C. A.
,
Dunson
D.
,
Jain
N.
,
Lee
J. K.
,
Miller
Y.
,
Ley
K.
.
2007
.
Induction of dendritic cell-like phenotype in macrophages during foam cell formation.
Physiol. Genomics
29
:
149
160
.
25
Jain
N.
,
Thatte
J.
,
Braciale
T.
,
Ley
K.
,
O’Connell
M.
,
Lee
J. K.
.
2003
.
Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays.
Bioinformatics
19
:
1945
1951
.
26
Jain
N.
,
Cho
H.
,
O’Connell
M.
,
Lee
J. K.
.
2005
.
Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data.
BMC Bioinformatics
6
:
187
.
27
Antonov
A. V.
,
Schmidt
T.
,
Wang
Y.
,
Mewes
H. W.
.
2008
.
ProfCom: a web tool for profiling the complex functionality of gene groups identified from high-throughput data.
Nucleic Acids Res.
36
:
W347
W351
.
28
Subramanian
A.
,
Tamayo
P.
,
Mootha
V. K.
,
Mukherjee
S.
,
Ebert
B. L.
,
Gillette
M. A.
,
Paulovich
A.
,
Pomeroy
S. L.
,
Golub
T. R.
,
Lander
E. S.
,
Mesirov
J. P.
.
2005
.
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.
Proc. Natl. Acad. Sci. U.S.A.
102
:
15545
15550
.
29
Gordon
S.
2007
.
The macrophage: past, present and future.
Eur. J. Immunol.
37
(
Suppl 1
):
S9
S17
.
30
Varin
A.
,
Mukhopadhyay
S.
,
Herbein
G.
,
Gordon
S.
.
2009
.
Alternative activation of macrophages by IL-4 impairs phagocytosis of pathogens but potentiates microbial-induced signalling and cytokine secretion.
Blood.
115
:
353
362
.
31
Martinez
F. O.
,
Sica
A.
,
Mantovani
A.
,
Locati
M.
.
2008
.
Macrophage activation and polarization.
Front. Biosci.
13
:
453
461
.
32
Sachais
B. S.
,
Kuo
A.
,
Nassar
T.
,
Morgan
J.
,
Kariko
K.
,
Williams
K. J.
,
Feldman
M.
,
Aviram
M.
,
Shah
N.
,
Jarett
L.
, et al
.
2002
.
Platelet factor 4 binds to low-density lipoprotein receptors and disrupts the endocytic machinery, resulting in retention of low-density lipoprotein on the cell surface.
Blood
99
:
3613
3622
.
33
Newby
A. C.
2008
.
Metalloproteinase expression in monocytes and macrophages and its relationship to atherosclerotic plaque instability.
Arterioscler. Thromb. Vasc. Biol.
28
:
2108
2114
.
34
Bengtsson
E.
,
Nilsson
J.
,
Jovinge
S.
.
2008
.
Cystatin C and cathepsins in cardiovascular disease.
Front. Biosci.
13
:
5780
5786
.
35
Koenen
R. R.
,
von Hundelshausen
P.
,
Nesmelova
I. V.
,
Zernecke
A.
,
Liehn
E. A.
,
Sarabi
A.
,
Kramp
B. K.
,
Piccinini
A. M.
,
Paludan
S. R.
,
Kowalska
M. A.
, et al
.
2009
.
Disrupting functional interactions between platelet chemokines inhibits atherosclerosis in hyperlipidemic mice.
Nat. Med.
15
:
97
103
.