IL-4 and IL-13 are prototypic Th2 cytokines that generate an “alternatively activated” phenotype in macrophages. We used high-density oligonucleotide microarrays to investigate the transcriptional profile induced in human monocytes by IL-13. After 8-h stimulation with IL-13, 142 genes were regulated (85 increased and 57 decreased). The majority of these genes were related to the inflammatory response and innate immunity; a group of genes related to lipid metabolism was also identified, with clear implications for atherosclerosis. In addition to characteristic markers of alternatively activated macrophages, a number of novel IL-13-regulated genes were seen. These included various pattern recognition receptors, such as CD1b/c/e, TLR1, and C-type lectin superfamily member 6. Several components of the IL-1 system were regulated. IL-1RI, IL-1RII, and IL-1Ra were all up-regulated, whereas the IL-1β-converting enzyme, caspase 1, and IRAK-M were down-regulated. LPS-inducible caspase 1 enzyme activity was also reduced in IL-13-stimulated monocytes, with a consequent decrease in pro-IL-1β processing. These data reveal that IL-13 has a potent effect on the transcriptional profile in monocytes. The IL-13-induced modulation of genes related to IL-1 clearly highlights the tightly controlled and complex levels of regulation of the production and response to this potent proinflammatory cytokine.

Monocytes and macrophages (Mφ)4 play a central role in both innate and adaptive immunity. They constitute a nonspecific first line of defense by phagocytosing opsonized or nonopsonized microorganisms; they can also act as APCs, thereby stimulating a specific immune response. Monocytes are derived from CD34+ myeloid progenitor cells in the bone marrow, and subsequently leave the bone marrow to circulate in the bloodstream (1). Inflammation due to tissue damage or infection results in the production of cytokines, chemokines, and other inflammatory mediators, which can influence monocyte function, causing recruitment to the site of inflammation and differentiation into Mφ. Different subpopulations of activated Mφ exist, depending on the type of stimulus they receive. Mφ are currently divided into “classically activated,” “type 2-activated,” and “alternatively activated” populations (see Refs.2, 3, 4 for recent review).

In classical activation, exposure of Mφ to IFN-γ primes the cells to respond to further stimulation by TNF-α or an inducer of TNF-α, frequently LPS or other bacterially derived products. These cells secrete various cytokines and chemokines including TNF-α, IL-12, IL-6, and CCL2; they up-regulate expression of MHC class II and CD86, and they produce NO and O2 (3, 5, 6, 7). These cells are particularly important for killing and degrading intracellular pathogens.

Type 2-activated Mφ arise from FcγR ligation followed by stimulation of TLR, CD40 or CD44. These cells produce many of the cytokines seen in classically activated Mφ (e.g., TNF-α and IL-6), but they switch off IL-12 production and secrete large quantities of IL-10 (8, 9, 10). These cells therefore exert a potent anti-inflammatory effect, and because IL-10 can stimulate IL-4 production by T cells, they also preferentially induce a Th2 response (11).

In contrast, alternatively activated Mφ are induced by IL-4, IL-13, or glucocorticoids. They secrete IL-10 and IL-1Ra and have increased expression of scavenger receptor and mannose receptor, but they are poor producers of reactive oxygen species or NO (3). Thus, these cells are unable to efficiently kill intracellular pathogens. The up-regulation of mannose receptor (12) may increase the potential for alternatively activated Mφ to present Ag, as has been shown for dendritic cells (DC) (13, 14), but these Mφ can also inhibit the proliferation of T cells under certain circumstances (15).

Various cell types produce IL-4 and IL-13, including Th2 cells, mast cells, and basophils; they play an important role in Th2 inflammation, particularly in the pathogenesis of allergy, asthma, atopic dermatitis, and also inhibition of certain forms of autoimmunity (2). They have a similar three-dimensional structure and share receptor complexes (16). As a result, these two cytokines signal through common components; IL-13 binding causes activation of JAK1 and Tyk2, which in turn causes phosphorylation of cytoplasmic tyrosines in the IL-4Rα chain. Crucially, this allows the recruitment of STAT6 to the receptor and subsequent phosphorylation/activation; STAT6 can then dimerize, translocate to the nucleus, and activate transcription of target genes (see Hershey (17) for comprehensive review). IL-4 and IL-13 therefore have overlapping but pleiotropic functions, which include enhancing B cell proliferation and isotype-switching, antagonizing the effects of IFN-γ, inducing the differentiation of DC (in combination with GM-CSF), and affecting T cell proliferation and differentiation (for IL-4, but not IL-13). They can also act on nonhemopoietic cells, including endothelial cells and smooth muscle cells where IL-13 stimulation enhances the production of CXCL8, CCL2, and CCL5 (18, 19).

To elucidate the effects of IL-13 in the early stages of the differentiation pathway to an alternatively activated Mφ phenotype, freshly isolated human monocytes were stimulated with IL-13, and their transcriptional profile was investigated using high-density oligonucleotide microarray analysis.

Validation of this analysis was provided by the identification of genes such as mannose receptor (MRC1), CD23, and 15-lipoxygenase (ALOX15), which are known to be modulated by IL-13 in monocytes/Mφ (12, 20, 21). Microarray analysis highlighted the regulation of many new genes involved in Ag presentation, host-pathogen interactions, and also lipid metabolism, including CD1b/c/e, TLR1, and DHCR24. The most striking results were those showing a very complex regulation of the components of the IL-1 system. At least six different genes involved in IL-1β production, signal transduction, and biological activity were regulated, with some of them not previously identified as genes associated with the alternative activated phenotype.

Monocytes were isolated from buffy coats from healthy donors, obtained through the Centro Trasfusionale (Ospedale Sacco, Milan, Italy). Blood was washed with pyrogen-free saline (SALF) and spun at 250 × g for 10 min to remove plasma and platelets, then loaded on Ficoll and spun at 600 × g for 25 min. The PBMC layer was collected, the cells were washed twice in saline and then resuspended in 285-mOsm RPMI 1640 (Biochrom) supplemented with 10% FCS (HyClone Laboratories). Monocytes were then isolated by loading on 46% (v/v) iso-osmotic Percoll and spinning at 750 × g for 25 min. The obtained monocyte population was ∼65% pure according to flow cytometry; monocytes were further purified using a MACS monocyte isolation kit (Miltenyi Biotec), according to the manufacturer’s instructions. The cells obtained after MACS were >95% pure according to flow cytometry; briefly, 1 × 105 cells were washed in PBS supplemented with 1% BSA and 0.01% NaN3 (FACS buffer). Cells were then resuspended in 100 μl of FACS buffer, and 10 μg of human IgG (Sigma-Aldrich) was added to block FcRs. After 15-min incubation at room temperature, FITC-conjugated anti-CD14 Ab or IgG1 isotype control Ab (both from Serotec) was added to a concentration of 10 μg/ml, and the cells were incubated for 30 min on ice. Cells were then washed twice in FACS buffer before analysis on a FACSCalibur flow cytometer (BD Biosciences) using CellQuest software.

Five milliliters of pure monocytes were seeded in nonadherent hydrophobic petriperm dishes (Sigma-Aldrich) at a concentration of 2 × 106 cells/ml in RPMI 1640/10% FCS and incubated at 37°C for 1 h. The cells were then stimulated with IL-13 at a concentration of 20 ng/ml for 2 or 8 h. Human IL-13 was a kind gift from Dr. A. Minty (Sanofi Elf Bio Recherches, Labège, France). In some experiments, LPS from Escherichia coli strain 055:B5 (Difco Laboratories) was added at a concentration of 100 ng/ml for the final 4 h of culture.

cRNA was generated according to the instructions provided by Affymetrix. Total RNA was extracted from 1 × 107 monocytes using TRIzol (Invitrogen Life Technologies), according to the manufacturer’s instructions, then DNase-treated using the DNase I Amplification-Grade kit (Invitrogen Life Technologies). The volume of DNase-treated total RNA was adjusted to 100 μl, then further purified using the RNeasy Mini-kit (Qiagen), precipitated using a standard ethanol precipitation, and resuspended in diethyl pyrocarbonate-treated H2O. Six micrograms of total RNA were used to synthesize double-stranded cDNA using the Superscript Double-Stranded cDNA Synthesis kit (Invitrogen Life Technologies) according to themanufacturer’s instructions, except that a T7-(dT)24 oligonucleotide (5′-GGCCAGTGAATTGTAATACGACTCACTATAGGGAGGCGGT24-3′; Genset) was used in place of the oligo provided with the kit. The cDNA was purified using a standard phenol-chloroform extraction followed by ethanol precipitation. cRNA was then synthesized using the BioArray High Efficiency RNA Transcript Labeling kit (Enzo Life Sciences), cleaned up using the Qiagen RNeasy Mini Kit and ethanol precipitation, and fragmented, before microarray analysis.

Fragmented cRNA was hybridized to Affymetrix HG-U133A genechips (Affymetrix), and then washed and scanned, according to the manufacturer’s guidelines. These genechips contain 22,283 probe sets, corresponding to almost 15,000 genes. Monocytes from six individual donors were analyzed after 8-h incubation in the presence or absence of IL-13 (20 ng/ml). Monocytes from three of these donors were also analyzed after 2-h stimulation with IL-13. To define the IL-13-dependent transcriptional profile, expression measures were computed using robust multiarray average (RMA) after quantiles normalization of the probe level data (22, 23). Differential expression was assessed by t test (p < 0.05), and type II error was controlled by applying a false detection rate (FDR) function (24). All of the above computations were conducted using the R statistics programming environment available at 〈www.r-project.org〉. Genes were considered to be differentially regulated in IL-13-stimulated cells compared with control cells if they had a log intensity average difference of 1.0, corresponding to a fold change of 2.0. Gene Ontology (GO) data mining (25) for biological process at level 3, and Expression Analysis Systematic Explorer (EASE) biological theme analysis (26) were conducted online at 〈http://david.niaid.nih.gov〉 using DAVID (27). Identification of potential transcription factor (TF) binding sites was performed using Toucan (28). Briefly, for each gene, the genomic sequence comprising 2000 bp upstream of, and 200 bp within the first exon was obtained from Ensembl. These sequences were then examined using the MotifScanner function in Toucan, using the Transfac 6.0 public Vertebrates TF matrix (29), with a stringent priority level of 0.1 and a Human Third Order background model.

RNA was purified as described above, and 2 μg were used to synthesize single-stranded cDNA using the Superscript First-Strand Synthesis System for RT-PCR (Invitrogen Life Technologies), according to the manufacturer’s instructions. Real-time quantitative RT-PCR was then performed using the SYBR Green PCR Master Mix (Applied Biosystems) with forward and reverse primers at a final concentration of 300 nM (GAPDH primers were used at 200 nM), in a sample volume of 25 μl. Primers for caspase 1 and CX3CR1 were a kind gift from P. Perrier and F. Marchesi, respectively (both from Istituto Mario Negri, Milan, Italy). The remaining primers were designed using Primer 3.0 software (30) from mRNA sequences submitted to GenBank, and are listed in Table I. PCR was conducted using a GeneAmp 5700 Sequence Detection System (Applied Biosystems) under the following cycling conditions: 2 min at 50°C (one cycle), 10 min at 95°C (one cycle), 15 s at 95°C, and 1 min at 60°C (40 cycles). For each gene (performed in duplicate for each sample), cycle threshold (Ct) values were determined from the linear region of the amplification plot and normalized by subtraction of the Ct value for GAPDH (generating a ΔCt value). The response to IL-13 was determined by subtraction of the ΔCt value for the time-matched control from the ΔCt value for the IL-13-stimulated sample (ΔΔCt value). Fold change was subsequently calculated using the formula 2ΔΔCt (where ΔΔCt was converted to an absolute value), and down-regulated genes were arbitrarily assigned a negative fold change. For statistical analysis, a two-tailed paired t test was performed comparing the ΔCt values for IL-13-stimulated and control samples. Between three and eight donors were investigated.

Table I.

Primer pairs used for real-time quantitative RT-PCRa

GenePrimersProduct (bp)
Caspase 1 (CASP1For 5′-ggaatgtcaagctttgctccct-3′ 103 
 Rev 5′-aagacgtgtgcggcttgactt-3′  
Catenin, α-like 1 (CTNNAL1For 5′-atttcaggtgactggccaac-3′ 114 
 Rev 5′-ggattcccaagcttcacaaa-3′  
CD163 For 5′-ttgccagcagcttaaatgtg-3′ 111 
 Rev 5′-ctcagtcccagtgcagtgaa-3′  
CD1C For 5′-tctcttgggtctcctggatg-3′ 118 
 Rev 5′-catgacaaaccagcaacagc-3′  
CX3CR1 For 5′-tgatttggctgaggcctgttat-3′ 63 
 Rev 5′-ggacaggaacacagtcccaaag-3′  
CXCR1 For 5′-ctcctgttcatgcccatacc-3′ 97 
 Rev 5′-cctcagggtgaagctgagac-3′  
CXCR2 For 5′-catggcttgatcagcaagga-3′ 113 
 Rev 5′-gctgcacttaggcaggaggt-3′  
CXCR4 For 5′-gaagctgttggctgaaaagg-3′ 96 
 Rev 5′-ctcactgacgttggcaaaga-3′  
Dual specificity phosphatase 10 (DUSP10For 5′-atcttgcccttcctgttcct-3′ 110 
 Rev 5′-gaggggaagatgagtggtga-3′  
Fatty acid desaturase 1 (FADS1For 5′-gcacctcaaagtggaaccat-3′ 148 
 Rev 5′-gggatgcatgttgatgtctg-3′  
Glyceraldehyde 3-phosphate dehydrogenase (GAPDHFor 5′-gatcatcagcaatgcctcct-3′ 99 
 Rev 5′-tgtggtcatgagtccttcca-3′  
Homer homolog 2 (HOMER2For 5′-tctgccgtgatgagaatgac-3′ 104 
 Rev 5′-tcttcagctgcgtgttcttc-3′  
IL-1R antagonist (IL1RaFor 5′-tgggggttctttcttcctct-3′ 99 
 Rev 5′-gaggcacagccatctttcat-3′  
IL-1R type 1 (IL-1RIFor 5′-aagtgggtggatcaccagag-3′ 103 
 Rev 5′-ccaccatgcctagctcattt-3′  
IL-1R type 2 (IL-1RIIFor 5′-tttcactggccttcttggtt-3′ 99 
 Rev 5′-tgaggccatagcacagtcag-3′  
Jagged 1 (JAG1For 5′-aaggggtgcggtatatttcc-3′ 106 
 Rev 5′-tcccgtgaagcctttgttac-3′  
Matrix metalloproteinase 9 (MMP9For 5′-agtccacccttgtgctcttc-3′ 105 
 Rev 5′-tctgccacccgagtgtaac-3′  
Peroxisome proliferative activated receptorγ (PPARγ) For 5′-gctggcctccttgatgaata-3′ 116 
 Rev 5′-ttgggctccataaagtcacc-3′  
Putative lymphocyte G0/G1 switch gene (G0S2For 5′-taccacaagcatccaccaaa-3′ 131 
 Rev 5′-tccttcctccctagtgcaaa-3′  
Wingless-type MMTV integration site family member 5A (WNT5AFor 5′-agcaacctcgtttctgagga-3′ 136 
 Rev 5′-aatgccctctccacaaagtg-3′  
GenePrimersProduct (bp)
Caspase 1 (CASP1For 5′-ggaatgtcaagctttgctccct-3′ 103 
 Rev 5′-aagacgtgtgcggcttgactt-3′  
Catenin, α-like 1 (CTNNAL1For 5′-atttcaggtgactggccaac-3′ 114 
 Rev 5′-ggattcccaagcttcacaaa-3′  
CD163 For 5′-ttgccagcagcttaaatgtg-3′ 111 
 Rev 5′-ctcagtcccagtgcagtgaa-3′  
CD1C For 5′-tctcttgggtctcctggatg-3′ 118 
 Rev 5′-catgacaaaccagcaacagc-3′  
CX3CR1 For 5′-tgatttggctgaggcctgttat-3′ 63 
 Rev 5′-ggacaggaacacagtcccaaag-3′  
CXCR1 For 5′-ctcctgttcatgcccatacc-3′ 97 
 Rev 5′-cctcagggtgaagctgagac-3′  
CXCR2 For 5′-catggcttgatcagcaagga-3′ 113 
 Rev 5′-gctgcacttaggcaggaggt-3′  
CXCR4 For 5′-gaagctgttggctgaaaagg-3′ 96 
 Rev 5′-ctcactgacgttggcaaaga-3′  
Dual specificity phosphatase 10 (DUSP10For 5′-atcttgcccttcctgttcct-3′ 110 
 Rev 5′-gaggggaagatgagtggtga-3′  
Fatty acid desaturase 1 (FADS1For 5′-gcacctcaaagtggaaccat-3′ 148 
 Rev 5′-gggatgcatgttgatgtctg-3′  
Glyceraldehyde 3-phosphate dehydrogenase (GAPDHFor 5′-gatcatcagcaatgcctcct-3′ 99 
 Rev 5′-tgtggtcatgagtccttcca-3′  
Homer homolog 2 (HOMER2For 5′-tctgccgtgatgagaatgac-3′ 104 
 Rev 5′-tcttcagctgcgtgttcttc-3′  
IL-1R antagonist (IL1RaFor 5′-tgggggttctttcttcctct-3′ 99 
 Rev 5′-gaggcacagccatctttcat-3′  
IL-1R type 1 (IL-1RIFor 5′-aagtgggtggatcaccagag-3′ 103 
 Rev 5′-ccaccatgcctagctcattt-3′  
IL-1R type 2 (IL-1RIIFor 5′-tttcactggccttcttggtt-3′ 99 
 Rev 5′-tgaggccatagcacagtcag-3′  
Jagged 1 (JAG1For 5′-aaggggtgcggtatatttcc-3′ 106 
 Rev 5′-tcccgtgaagcctttgttac-3′  
Matrix metalloproteinase 9 (MMP9For 5′-agtccacccttgtgctcttc-3′ 105 
 Rev 5′-tctgccacccgagtgtaac-3′  
Peroxisome proliferative activated receptorγ (PPARγ) For 5′-gctggcctccttgatgaata-3′ 116 
 Rev 5′-ttgggctccataaagtcacc-3′  
Putative lymphocyte G0/G1 switch gene (G0S2For 5′-taccacaagcatccaccaaa-3′ 131 
 Rev 5′-tccttcctccctagtgcaaa-3′  
Wingless-type MMTV integration site family member 5A (WNT5AFor 5′-agcaacctcgtttctgagga-3′ 136 
 Rev 5′-aatgccctctccacaaagtg-3′  
a

The expected PCR product size for each gene is also shown. For, Forward; Rev, reverse.

The concentration of human IL-1β in cell culture supernatants was measured using a Human IL-1β colorimetric ELISA (Endogen) according to the manufacturer’s instructions.

Relative levels of active caspase 1 activity were determined by flow cytometry using FAM-fluorochrome inhibitor of caspases (FLICA) reagent (Immunochemistry Technologies) according to the manufacturer’s instructions. Briefly, 300 μl of monocytes were incubated with the FAM-YVAD-fluoromethylketone reagent for 1 h at 37°C. The FAM-FLICA reagent is cell permeable and binds covalently to active intracellular caspase 1. Unbound reagent was removed by two washes in wash buffer. The cells were then resuspended in 300 μl of wash buffer and propidium iodide was added, to distinguish dead cells. The cells were then analyzed on a FACSCalibur flow cytometer, gating on the live cells, and measuring the fluorescence due to the presence of FAM-FLICA bound to caspase 1.

Monocytes were cultured as described above. After 8 h of culture, 1-ml aliquots were removed onto ice and centrifuged at 13,000 rpm for 10 s in a microfuge. The cell pellets were washed twice with 1 ml of ice-cold PBS containing 20 mM NaF (0.5 M stock; Sigma-Aldrich), 1 mM Na3VO4 (0.2 M stock; Sigma-Aldrich), and β-glycerophosphate (0.5 M stock; Sigma-Aldrich). The cells were then lysed in lysis buffer, containing 50 mM Tris-HCl (pH 8.0), 1% Triton X-100, 100 mM NaCl, 1 mM MgCl2, 1 mM Na3VO4, 20 mM NaF, 1 mM β-glycerophosphate, 25 μg/ml aprotinin, 25 μg/ml pepstatin A, and 50 μg/ml leupeptin (all from Sigma-Aldrich). Then, 100 μl of ice-cold lysis buffer was added to each aliquot of 2 × 106 cells. Cells were lysed by pipetting up and down, and then genomic DNA was sheared by repeatedly passing the lysate through a 25-gauge needle connected to a 1-ml syringe. The protein concentration of each lysate was determined using a Micro BCA Protein Assay Reagent kit (Pierce) in microplate format, according to the manufacturer’s instructions. Lysates were adjusted to 1 μg/μl and stored at −70°C.

A total of 15 μg of each lysate were run on 12% SDS-acrylamide gels, and then the protein was transferred to nitrocellulose membrane (Amersham Biosciences) using the Mini Trans-Blot Electrophoretic Transfer Cell (Bio-Rad), according to the manufacturer’s instructions. The membrane was probed for IL-1β using an anti-human IL-1β Ab (Cell Signaling Technology) followed by an HRP-conjugated donkey anti-rabbit secondary Ab (Amersham Biosciences), according to the manufacturer’s instructions. Specific Ab binding was detected using ECL Western Blotting Detection Reagents (Amersham Biosciences) followed by exposure to x-ray film.

Freshly isolated human monocytes (>95% pure by flow cytometry) were stimulated with 20 ng/ml IL-13 for 2 or 8 h; this concentration of IL-13 was previously shown to be optimal for stimulating a variety of responses in human monocytes (31). The transcriptional profile was then determined by microarray analysis using Affymetrix HG-U133A genechips (consisting of 22,283 probe sets, corresponding to ∼15,000 genes); three donors were analyzed at the 2-h time point, and six donors were analyzed at the 8-h time point, using 8-h unstimulated monocytes as the baseline control.

IL-13 had a potent effect on the monocyte transcriptional profile: after 8-h stimulation, 442 regulated genes were identified following the initial RMA analysis (see additional information at 〈www.marionegri.it/profiles〉), and this was reduced to 142 regulated genes after restricting the profile to those genes with a fold change of ≥2 (Table II). Of the 142 genes affected after 8-h IL-13 stimulation, 85 were up-regulated (with a maximum fold change of 22.6) and 57 were down-regulated (with a maximum fold change of 11.3). The majority of these genes have been characterized; only 11 genes were unidentified or hypothetical. According to the microarray analysis, the maximally regulated genes were fatty acid binding protein 4 (FABP4; increased expression) and ADAM-like Decysin 1 (ADAMDEC1; decreased expression).

Many of the genes regulated after 8-h IL-13 stimulation have been previously identified as being regulated by IL-13 or IL-4 in monocytes/Mφ, demonstrating the validity of our experimental protocol. These genes include mannose receptor (MRC1), CD23 (FCER2; FcR for IgE), CCL22 (also known as MDC), arachidonate 15-lipoxygenase (ALOX15), IL-1RII, and IL-1Ra (12, 20, 21, 31, 32, 33).

Two hours of IL-13 stimulation had a greater effect on the transcriptional profile, with 638 genes regulated with a fold change of >2 (435 genes up-regulated; 203 genes down-regulated). For the purposes of clarity, these genes are not described in detail in this paper, but the full list is freely available at 〈www.marionegri.it/profiles〉. However, 70 of the genes regulated at 2 h (with a fold change >2) were also regulated at 8 h, as shown in Table II; an additional six genes had a fold change <2, whereas the remainder were unchanged at 2 h but regulated at 8 h.

Hierarchical clustering of the genes regulated at 8 h (including their observed expression at 2 h) using Euclidean distances after median centering of their eisen expression values, revealed six major clusters (Fig. 1). Genes in cluster 1 had high expression levels after 8-h IL-13 stimulation, compared with the global median expression; this cluster was further divided according to whether genes had low expression at 2 h (cluster 1A) or not (cluster 1B). Genes in cluster 2 had relatively low expression in the unstimulated cells, but higher expression after 2- and 8-h IL-13 stimulation; again, this cluster was further subdivided depending on the expression levels at 2 h. Genes in cluster 3 had high expression in unstimulated cells and low expression after 2- and 8-h IL-13 stimulation. Finally, cluster 4 genes had high expression in unstimulated cells and after 2-h IL-13 stimulation, followed by low expression after 8-h IL-13 stimulation.

FIGURE 1.

Hierarchical clustering (using Euclidean distances) of the median-centered eisen expression values. The 142 genes that were regulated after 8-h IL-13 stimulation were hierarchically clustered, including their observed expression at 2 h. Six major clusters were observed, with a similar pattern of regulation. Cluster 1A principally contained genes involved with lipid metabolism, whereas the remaining clusters were a mix of genes. Expression is indicated by a color scale from low (green) to high (red).

FIGURE 1.

Hierarchical clustering (using Euclidean distances) of the median-centered eisen expression values. The 142 genes that were regulated after 8-h IL-13 stimulation were hierarchically clustered, including their observed expression at 2 h. Six major clusters were observed, with a similar pattern of regulation. Cluster 1A principally contained genes involved with lipid metabolism, whereas the remaining clusters were a mix of genes. Expression is indicated by a color scale from low (green) to high (red).

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GO data mining for biological process (at level 3) suggests that the transcriptional profile induced by IL-13 is principally related to signal transduction, response to a biotic stimulus, and protein metabolism (Fig. 2). However, using the online version of the EASE (available at 〈http://david.niaid.nih.gov/david〉), which performs a statistical analysis of gene categories in the gene list to find those categories that are the most overrepresented (and can therefore be described as “themes” of the gene list), reveals a trend toward immunity (e.g., inflammatory response, innate immune response, response to pest/pathogen/parasite, etc.), as might be expected of IL-13-regulated genes (Table III).

FIGURE 2.

GO data mining. The 142 regulated genes were characterized according to their biological process classification (at level 3) in the GO database (25 ). Thirty-three percent of the genes did not have a GO classification. The majority of the remaining genes were involved with signal transduction and response to a biotic stimulus.

FIGURE 2.

GO data mining. The 142 regulated genes were characterized according to their biological process classification (at level 3) in the GO database (25 ). Thirty-three percent of the genes did not have a GO classification. The majority of the remaining genes were involved with signal transduction and response to a biotic stimulus.

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Table III.

EASE overrepresentation analysis of the genes listed in Table II a

SystemCategoryEASE Score
Biological process Inflammatory response 5.4 × 10−9 
Biological process Innate immune response 7.0 × 10−9 
Biological process Response to pest/pathogen/parasite 2.6 × 10−8 
Biological process Immune response 2.7 × 10−7 
Biological process Response to wounding 4.6 × 10−7 
Biological process Defense response 1.2 × 10−6 
Biological process Response to biotic stimulus 1.6 × 10−6 
Biological process Response to stress 8.8 × 10−6 
Biological process Response to external stimulus 6.6 × 10−5 
Molecular function Hemopoietin/IFN-class cytokine receptor activity 0.00028 
Molecular function Cytokine binding 0.00033 
Biological process Antimicrobial humoral response 0.00041 
Biological process Humoral defense mechanism 0.00041 
Biological process Antimicrobial humoral response 0.00079 
Cellular component Integral to membrane 0.00082 
Cellular component Plasma membrane 0.0022 
Molecular function Defense/immunity protein activity 0.0024 
Biological process Humoral immune response 0.0026 
Molecular function Signal transducer activity 0.0027 
Molecular function Endopeptidase inhibitor activity 0.0027 
Molecular function Protease inhibitor activity 0.0029 
Molecular function IL binding 0.0035 
Molecular function IL receptor activity 0.0035 
Biological process Response to chemical substance 0.0037 
Molecular function Growth factor binding 0.0062 
Cellular component Membrane 0.0075 
Molecular function Enzyme inhibitor activity 0.0087 
Molecular function Receptor activity 0.0093 
Molecular function Oxidoreductase activity 0.012 
Biological process Lipid metabolism 0.016 
Molecular function Enzyme regulator activity 0.018 
Biological process Cell communication 0.020 
Molecular function Catalytic activity 0.022 
Biological process Fatty acid metabolism 0.027 
Molecular function Receptor signaling protein activity 0.029 
Biological process Chemotaxis 0.032 
Biological process Taxis 0.032 
Cellular component Integral to plasma membrane 0.033 
Molecular function Metallopeptidase activity 0.038 
Molecular function Metalloendopeptidase activity 0.043 
Biological process Alcohol metabolism 0.043 
Biological process N-linked glycosylation 0.045 
Molecular function Metal ion binding 0.045 
SystemCategoryEASE Score
Biological process Inflammatory response 5.4 × 10−9 
Biological process Innate immune response 7.0 × 10−9 
Biological process Response to pest/pathogen/parasite 2.6 × 10−8 
Biological process Immune response 2.7 × 10−7 
Biological process Response to wounding 4.6 × 10−7 
Biological process Defense response 1.2 × 10−6 
Biological process Response to biotic stimulus 1.6 × 10−6 
Biological process Response to stress 8.8 × 10−6 
Biological process Response to external stimulus 6.6 × 10−5 
Molecular function Hemopoietin/IFN-class cytokine receptor activity 0.00028 
Molecular function Cytokine binding 0.00033 
Biological process Antimicrobial humoral response 0.00041 
Biological process Humoral defense mechanism 0.00041 
Biological process Antimicrobial humoral response 0.00079 
Cellular component Integral to membrane 0.00082 
Cellular component Plasma membrane 0.0022 
Molecular function Defense/immunity protein activity 0.0024 
Biological process Humoral immune response 0.0026 
Molecular function Signal transducer activity 0.0027 
Molecular function Endopeptidase inhibitor activity 0.0027 
Molecular function Protease inhibitor activity 0.0029 
Molecular function IL binding 0.0035 
Molecular function IL receptor activity 0.0035 
Biological process Response to chemical substance 0.0037 
Molecular function Growth factor binding 0.0062 
Cellular component Membrane 0.0075 
Molecular function Enzyme inhibitor activity 0.0087 
Molecular function Receptor activity 0.0093 
Molecular function Oxidoreductase activity 0.012 
Biological process Lipid metabolism 0.016 
Molecular function Enzyme regulator activity 0.018 
Biological process Cell communication 0.020 
Molecular function Catalytic activity 0.022 
Biological process Fatty acid metabolism 0.027 
Molecular function Receptor signaling protein activity 0.029 
Biological process Chemotaxis 0.032 
Biological process Taxis 0.032 
Cellular component Integral to plasma membrane 0.033 
Molecular function Metallopeptidase activity 0.038 
Molecular function Metalloendopeptidase activity 0.043 
Biological process Alcohol metabolism 0.043 
Biological process N-linked glycosylation 0.045 
Molecular function Metal ion binding 0.045 
a

Categories with the lowest EASE score are significantly overrepresented in the list. Also shown is the GO system to which each category belongs.

Table II.

List of genes regulated in human monocytes after 8-h stimulation with IL-13 (20 ng/ml)a

GenBank Accession No.Gene SymbolGene DescriptionFold ChangeCluster
2 h8 h
Cell cycle, cell proliferation, or differentiation      
 NM_014479 ADAMDEC1 ADAM-like, decysin 1 −5.3 −11.3 
 D38553 BRRN1 Barren homolog (Drosophila1.7 2.0 2A 
 AD000092 DNASE2 Deoxyribonuclease II, lysosomal NC −2.0 
 NM_005103 FEZ1 Fasciculation and elongation proteinζ 1 (zygin 1) −2.6 −2.6 
 NM_021731 FZR1 Fzr1 protein 2.8 3.5 2A 
 NM_015714 GOS2 Putative lymphocyte G0/G1 switch gene NC 4.6 1B 
 NM_000820 GAS6 Growth arrest-specific 6 2.1 4.0 2A 
 BC006454 GAS7 Growth arrest-specific 7 NC −3.0 
 NM_002430 MN1 Meningioma (disrupted in balanced translocation) 1 2.6 −4.0 
 NM_006197 PCM1 Pericentriolar material 1 NC 2.8 2A 
 NM_016205 PDGFC Platelet-derived growth factor C 3.5 2.3 2A 
 NM_002826 QSCN6 Quiescin Q6 4.3 2.6 2A 
 NM_002615 SERPINF1 Serine (or cysteine) proteinase inhibitor, clade F, member 1 NC −2.6 
 NM_003710 SPINT1 Serine protease inhibitor, Kunitz type 1 NC 3.7 1B 
 AF027205 SPINT2 Serine protease inhibitor, Kunitz type 2 4.3 5.3 2A 
 NM_006520 TCTE1L T-complex-associated-testis-expressed 1-like −1.8 −2.3 
 NM_003392 WNT5A Wingless-type MMTV integration site family, member 5A 10.6 15.0 2A 
Cytokines or complement      
 NM_000064 C3 Complement component 3 −3.5 −2.3 
 NM_002990 CCL22 Chemokine (C-C motif) ligand 22 NC 2.3 1B 
 AW083357 IL1RN IL-1R antagonist 9.2 7.0 2A 
Membrane receptors or transporter molecules      
 AF285167 ABCA1 ATP-binding cassette, sub-family A (ABC1), member 1 NC −3.5 
 U62027 C3AR1 Complement component 3a receptor 1 NC −2.8 
 AF290886 CD209 CD209 Ag NC 3.3 1B 
 NM_001774 CD37 CD37 Ag 2.5 −2.1 
 NM_001778 CD48 CD48 Ag (B cell membrane protein) NC −2.8 
 4867982 CDW52 CDW52 Ag (CAMPATH-1 Ag) NC 3.7 2A 
 NM_001557 CXCR2 IL-8R β NC 2.3 1B 
 AF348491 CXCR4 Chemokine (C-X-C motif) receptor 4 NC −3.7 
 AF056979 IFNGR1 IFN-γ receptor 1 NC −2.3 
 NM_004258 IGSF2 Ig superfamily member 2 NC −3.5 
 U62858 IL13RA1 IL-13R, α 1 −2.5 −2.6 
 NM_000877 IL1R1 IL-1R, type I 8.0 3.5 2A 
 U64094 IL1R2 IL-1R, type II NC 3.5 1B 
 NM_002183 IL3RA IL-3R, α (low affinity) 3.5 2.6 2A 
 U73191 KCNJ15 Potassium inwardly rectifying channel, subfamily J, member 15 NC −4.0 
 AF011565 LILRB2 Leukocyte Ig-like receptor, subfamily B, member 2 −1.4 −2.6 
 NM_024021 MS4A4A Membrane-spanning 4-domains, subfamily A, member 4 32.0 6.5 2B 
 AF035307 PLXNC1 Plexin C1 NC −4.3 
 NM_002958 RYK RYK receptor-like tyrosine kinase NC 2.0 2A 
 NM_004694 SLC16A6 Solute carrier family 16 (monocarboxylic acid transporters), member 6 NC −2.1 
 AF288410 SLC26A6 Solute carrier family 26, member 6 4.6 2.5 2B 
 NM_003982 SLC7A7 Solute carrier family 7 (cationic amino acid transporter), member 7 2.3 −2.5 
Pattern recognition receptors      
 NM_000591 CD14 CD14 Ag −3.5 −2.6 
 NM_004244 CD163 CD163 Ag −3.3 −3.7 
 NM_001764 CD1B CD1B Ag, b polypeptide NC 2.6 1B 
 NM_001765 CD1C CD1C Ag, c polypeptide NC 5.7 1B 
 AA309511 CD1E CD1E Ag, e polypeptide NC 6.5 1B 
 AF200738 CLECSF6 C-type lectin, superfamily member 6 4.3 2.8 2A 
 NM_014358 CLECSF9 C-type lectin, superfamily member 9 NC −2.0 
 NM_002002 FCER2 Fc fragment of IgE, low affinity II, receptor for CD23A NC 14.9 1B 
 NM_002438 MRC1 Mannose receptor, C type 1 7.5 18.4 2A 
 AL050262 TLR1 TLR NC −2.5 
Cytoskeleton      
 NM_003798 CTNNAL1 Catenin (cadherin-associated protein), α like 1 9.2 13.0 2A 
 NM_016337 EVL Enah/Vasp-like NC 5.3 1B 
 NM_000177 GSN Gelsolin (amyloidosis, Finnish type) NC 2.1 1A 
 4872688_RC HOM-TES-103 HOM-TES-103 tumor Ag-like NC −2.5 
 NM_014751 MTSS1 Metastasis suppressor 1 NC −2.3 
 AL046979 TNS Tensin NC −3.5 
     (Table continues
GenBank Accession No.Gene SymbolGene DescriptionFold ChangeCluster
2 h8 h
Cell cycle, cell proliferation, or differentiation      
 NM_014479 ADAMDEC1 ADAM-like, decysin 1 −5.3 −11.3 
 D38553 BRRN1 Barren homolog (Drosophila1.7 2.0 2A 
 AD000092 DNASE2 Deoxyribonuclease II, lysosomal NC −2.0 
 NM_005103 FEZ1 Fasciculation and elongation proteinζ 1 (zygin 1) −2.6 −2.6 
 NM_021731 FZR1 Fzr1 protein 2.8 3.5 2A 
 NM_015714 GOS2 Putative lymphocyte G0/G1 switch gene NC 4.6 1B 
 NM_000820 GAS6 Growth arrest-specific 6 2.1 4.0 2A 
 BC006454 GAS7 Growth arrest-specific 7 NC −3.0 
 NM_002430 MN1 Meningioma (disrupted in balanced translocation) 1 2.6 −4.0 
 NM_006197 PCM1 Pericentriolar material 1 NC 2.8 2A 
 NM_016205 PDGFC Platelet-derived growth factor C 3.5 2.3 2A 
 NM_002826 QSCN6 Quiescin Q6 4.3 2.6 2A 
 NM_002615 SERPINF1 Serine (or cysteine) proteinase inhibitor, clade F, member 1 NC −2.6 
 NM_003710 SPINT1 Serine protease inhibitor, Kunitz type 1 NC 3.7 1B 
 AF027205 SPINT2 Serine protease inhibitor, Kunitz type 2 4.3 5.3 2A 
 NM_006520 TCTE1L T-complex-associated-testis-expressed 1-like −1.8 −2.3 
 NM_003392 WNT5A Wingless-type MMTV integration site family, member 5A 10.6 15.0 2A 
Cytokines or complement      
 NM_000064 C3 Complement component 3 −3.5 −2.3 
 NM_002990 CCL22 Chemokine (C-C motif) ligand 22 NC 2.3 1B 
 AW083357 IL1RN IL-1R antagonist 9.2 7.0 2A 
Membrane receptors or transporter molecules      
 AF285167 ABCA1 ATP-binding cassette, sub-family A (ABC1), member 1 NC −3.5 
 U62027 C3AR1 Complement component 3a receptor 1 NC −2.8 
 AF290886 CD209 CD209 Ag NC 3.3 1B 
 NM_001774 CD37 CD37 Ag 2.5 −2.1 
 NM_001778 CD48 CD48 Ag (B cell membrane protein) NC −2.8 
 4867982 CDW52 CDW52 Ag (CAMPATH-1 Ag) NC 3.7 2A 
 NM_001557 CXCR2 IL-8R β NC 2.3 1B 
 AF348491 CXCR4 Chemokine (C-X-C motif) receptor 4 NC −3.7 
 AF056979 IFNGR1 IFN-γ receptor 1 NC −2.3 
 NM_004258 IGSF2 Ig superfamily member 2 NC −3.5 
 U62858 IL13RA1 IL-13R, α 1 −2.5 −2.6 
 NM_000877 IL1R1 IL-1R, type I 8.0 3.5 2A 
 U64094 IL1R2 IL-1R, type II NC 3.5 1B 
 NM_002183 IL3RA IL-3R, α (low affinity) 3.5 2.6 2A 
 U73191 KCNJ15 Potassium inwardly rectifying channel, subfamily J, member 15 NC −4.0 
 AF011565 LILRB2 Leukocyte Ig-like receptor, subfamily B, member 2 −1.4 −2.6 
 NM_024021 MS4A4A Membrane-spanning 4-domains, subfamily A, member 4 32.0 6.5 2B 
 AF035307 PLXNC1 Plexin C1 NC −4.3 
 NM_002958 RYK RYK receptor-like tyrosine kinase NC 2.0 2A 
 NM_004694 SLC16A6 Solute carrier family 16 (monocarboxylic acid transporters), member 6 NC −2.1 
 AF288410 SLC26A6 Solute carrier family 26, member 6 4.6 2.5 2B 
 NM_003982 SLC7A7 Solute carrier family 7 (cationic amino acid transporter), member 7 2.3 −2.5 
Pattern recognition receptors      
 NM_000591 CD14 CD14 Ag −3.5 −2.6 
 NM_004244 CD163 CD163 Ag −3.3 −3.7 
 NM_001764 CD1B CD1B Ag, b polypeptide NC 2.6 1B 
 NM_001765 CD1C CD1C Ag, c polypeptide NC 5.7 1B 
 AA309511 CD1E CD1E Ag, e polypeptide NC 6.5 1B 
 AF200738 CLECSF6 C-type lectin, superfamily member 6 4.3 2.8 2A 
 NM_014358 CLECSF9 C-type lectin, superfamily member 9 NC −2.0 
 NM_002002 FCER2 Fc fragment of IgE, low affinity II, receptor for CD23A NC 14.9 1B 
 NM_002438 MRC1 Mannose receptor, C type 1 7.5 18.4 2A 
 AL050262 TLR1 TLR NC −2.5 
Cytoskeleton      
 NM_003798 CTNNAL1 Catenin (cadherin-associated protein), α like 1 9.2 13.0 2A 
 NM_016337 EVL Enah/Vasp-like NC 5.3 1B 
 NM_000177 GSN Gelsolin (amyloidosis, Finnish type) NC 2.1 1A 
 4872688_RC HOM-TES-103 HOM-TES-103 tumor Ag-like NC −2.5 
 NM_014751 MTSS1 Metastasis suppressor 1 NC −2.3 
 AL046979 TNS Tensin NC −3.5 
     (Table continues

It is clear from the gene list in Table II that there are several genes involved with the cell cycle or cell differentiation. These include ADAM-like decysin 1 (ADAMDEC1) whose expression is increased during the in vitro differentiation of monocytes into Mφ, and further increased after classical LPS activation of these Mφ, but which is not expressed in immature DC (34). IL-13 decreases the expression of this molecule in monocytes, which could promote differentiation toward a DC phenotype rather than a Mφ phenotype. In contrast, Wnt5A is highly up-regulated by IL-13; this gene is important during hemopoiesis for controlling the phenotypic specialization of blood cells. Overexpression of Wnt5A in hemopoietic progenitor cells increases the proportion of erythrocytes and monocytes, while reducing the number of Mφ (35). In (mature) monocytes, induction of this gene by IL-13 could therefore play an important role in driving the differentiation of monocytes toward a DC phenotype.

Many of the IL-13-regulated genes are enzymes or other molecules involved with metabolism, such as phosphofructokinase (PFKP) and adenosine deaminase (ADA). Of interest, “lipid metabolism” had a significant EASE score (Table III), and several of the enzymes in Table II appear to have a role in regulation of fatty acids and/or cholesterol biosynthesis (according to their GO), such as fatty acid desaturase 1 and 2 (FADS1/2), acyl-coenzyme A dehydrogenase (ACADVL), 24-dehydrocholesterol reductase (DHCR24), and sterol-C4-methyloxidase-like (SC4MOL). Also, the transporter molecule ABCA1 has a role in cholesterol transport (36); regulation of these genes may have implications for the pathogenesis of atherosclerosis and foam cell formation, where a role for IL-13 has been suggested (20). Moreover, the majority of the lipid metabolism-related genes are closely associated in cluster 1A (Fig. 1); this shows that their expression pattern in response to IL-13 is very similar, and may therefore be under the control of a common signaling pathway downstream of IL-13. Analysis of TF binding sites using Toucan (see Materials and Methods) reveals the significant overrepresentation of potential binding sites for the TF NF-Y in cluster 1A (data not shown); this molecule has been implicated in the regulation of other genes in cholesterol biosynthesis (37, 38).

As suggested by the EASE results, a significant proportion of the IL-13-regulated genes have immunological relevance, including complement component 3 (C3), CCL22 (31), CXCR2 (39), CXCR4, IL13Rα1, IFNGR1, and IL3Rα (40). This analysis did not reveal any regulation of cytokines such as TNF-α, TGF-β, IL-1β, or IL-6 (2). Of particular interest are the large number of pattern recognition receptors: some of these genes, including MRC1, CD14, and CD23 (FcER2), are known to be regulated by IL-13 (12, 21, 41); however, IL-13 also up-regulates three members of the CD1 family (CD1b, -c, and -e) and a C-type lectin (CLECSF6), while down-regulating another C-type lectin (CLECSF9) and TLR1. CLECSF6 has previously been shown to be up-regulated by IL-13 in neutrophils (42). CD1b, -c, -e, and CD23 are located very closely together in cluster 1B (Fig. 1), suggesting a similar pattern of up-regulation by IL-13. The increased expression of CD1 could be of considerable interest in terms of lipid and glycolipid Ag presentation to T cells. Moreover, ligands that are bound by MRC1 could be internalized to late endosomes for subsequent presentation by CD1b (13), as has been shown in DC. This will be the subject of further investigation in our laboratory.

Clearly therefore, the transcriptional profile has a range of genes including TF, cytokine and chemokines and/or their receptors, other cell surface molecules, enzymes, and signal transduction components. Several of these genes (distributed throughout the different clusters shown in Fig. 1) were chosen for analysis by real-time quantitative RT-PCR, to confirm the results of the microarray analysis.

Real-time PCR was used to verify the up- or down-regulation of selected genes, using the primer pairs shown in Table I. In addition, three genes of interest to our laboratory were included that were not revealed by the microarray analysis: CXCR1, CX3CR1, and MMP9. There was a good agreement between the real-time PCR data and the Affymetrix data (Fig. 3), with confirmation of the up- or down-regulation of each gene; these data also had a similar pattern to the clustering seen in Fig. 1. For many of the genes, the fold change was also of a comparable magnitude, although CD1c, Wnt5A, and CTNNAL1 showed a massive up-regulation according to real-time PCR. The regulation of either mRNA or protein for CXCR2, IL-1RI, IL-1RII, IL-1Ra, and PPARγ has previously been demonstrated after IL-4 or IL-13 stimulation of human monocytes or Mφ (32, 33, 39, 43).

FIGURE 3.

Real-time quantitative RT-PCR for genes selected from the profile. Sixteen genes from the list in Table II (plus three additional genes that were not identified by microarray analysis) were analyzed by real-time PCR to confirm the genechip results (▦, 2 h; ▪, 8 h). Down-regulated genes were arbitrarily assigned a negative value. For the genes marked with a number sign (#), the expression at 2 h was not determined. ∗, p < 0.05.

FIGURE 3.

Real-time quantitative RT-PCR for genes selected from the profile. Sixteen genes from the list in Table II (plus three additional genes that were not identified by microarray analysis) were analyzed by real-time PCR to confirm the genechip results (▦, 2 h; ▪, 8 h). Down-regulated genes were arbitrarily assigned a negative value. For the genes marked with a number sign (#), the expression at 2 h was not determined. ∗, p < 0.05.

Close modal

Of interest, CXCR1 and CX3CR1 showed significant up- and down-regulation respectively, although these genes were not identified by microarray analysis. MMP9 expression was also reduced, although this was not quite statistically significant (p = 0.073). These data demonstrate that microarrays may not always identify genes that are known to be regulated. The most likely explanation for this discrepancy is due to interindividual variability—human blood donors can have marked differences in their gene expression, which obviously makes it more beneficial to have larger sample sizes. There could also be a problem with either the probe sets on the genechip (lack of sensitivity or specificity) or the subsequent analysis, although an alternative approach to the analysis (using the proprietary Affymetrix Microarray Suite 5.0 software) did not identify these genes either. Real-time PCR for other genes that are not listed in Table II, such as CCR2 and p75 TNFR, validated that these genes are not regulated (data not shown).

The increased expression of CXCR1/2 by IL-13 has previously been demonstrated in our laboratory (39) and has implications for the control of monocyte migration in a variety of inflammatory situations. The IL-13-dependent down-regulation of the chemokine receptors CXCR4 and CX3CR1 in human monocytes is novel, and again may have important implications in pathology. Work by Fraticelli et al. (76) showed that CX3CL1 (fractalkine, the ligand for CX3CR1) is important in polarized Th1/Th2 responses. IL-4/IL-13 blocked the induction of this chemokine by endothelial cells, and Th2 cells were shown to have lower expression of CX3CR1 than Th1 cells. The reduction in CX3CR1 expression in monocytes by IL-13 may also contribute to Th1/Th2 polarization; the functional significance of this regulation is the topic of current investigations in our laboratory.

A reduction in the expression of caspase 1 after 8 h of IL-13 stimulation was also confirmed by real-time PCR. Caspase 1 is also known as the IL-1β-converting enzyme (ICE), and is responsible for the proteolytic cleavage of pro-IL-1β to its mature form (44, 45). Because regulation of caspase 1 could therefore modulate IL-1β production in monocytes, we focused our attention on this gene.

According to the microarray analysis and real-time PCR, there was a ∼3-fold down-regulation of caspase 1 mRNA after 2-h stimulation with IL-13 and a 5-fold down-regulation after 8 h. Therefore, we investigated whether the reduction in mRNA levels corresponded to a reduction in the level of active caspase 1, using a FAM-FLICA assay (see Materials and Methods). Caspase 1 exists as a 45-kDa precursor and must itself be proteolytically cleaved into an active heterodimer composed of a 10- and 20-kDa chain, before it can act on IL-1β (see Ref.46 for review). Monocytes were stimulated for 4 h with IL-13, and then a further 4 h with the addition of LPS to induce active caspase 1; the cells were then stained with the FAM-FLICA reagent, and the level of active caspase 1 was determined by flow cytometry. As shown in Fig. 4, 100 ng/ml LPS alone caused an increase in caspase 1 activity compared with unstimulated control cells, as evaluated by an increase in mean channel fluorescence. Pretreatment of the cells with as little as 2 ng/ml IL-13 was sufficient to prevent the LPS-dependent increase in caspase 1 activity, suggesting that a reduction in mRNA levels has a subsequent effect on the capacity for generating active caspase 1.

FIGURE 4.

A, Caspase 1 activity in monocytes stimulated with LPS and/or IL-13. Monocytes were cultured for 8 h, in the presence or absence of IL-13 (at 2, 20, or 200 ng/ml). Caspase 1 activity was induced by the addition of LPS (100 ng/ml) for the final 4 h of culture, and the level of caspase 1 activity was measured by flow cytometry using FAM-FLICA reagent. LPS stimulation alone (gray shading) increased the level of caspase 1 activity relative to control cells or cells pretreated with IL-13 before LPS stimulation (black lines), as indicated. Results from one experiment are shown, representative of three independent experiments. B, Graphical representation of the mean results from the three independent experiments, showing the increase in caspase 1 activity relative to control cells and the abrogation of LPS-stimulated caspase 1 activation due to pretreatment with different concentrations of IL-13.

FIGURE 4.

A, Caspase 1 activity in monocytes stimulated with LPS and/or IL-13. Monocytes were cultured for 8 h, in the presence or absence of IL-13 (at 2, 20, or 200 ng/ml). Caspase 1 activity was induced by the addition of LPS (100 ng/ml) for the final 4 h of culture, and the level of caspase 1 activity was measured by flow cytometry using FAM-FLICA reagent. LPS stimulation alone (gray shading) increased the level of caspase 1 activity relative to control cells or cells pretreated with IL-13 before LPS stimulation (black lines), as indicated. Results from one experiment are shown, representative of three independent experiments. B, Graphical representation of the mean results from the three independent experiments, showing the increase in caspase 1 activity relative to control cells and the abrogation of LPS-stimulated caspase 1 activation due to pretreatment with different concentrations of IL-13.

Close modal

Cell lysates from the above stimulated monocytes (4 h with or without IL-13 followed by 4 h with or without LPS) were probed for IL-1β by Western blotting, while supernatants were collected for subsequent ELISA. As has been shown previously for IL-4/IL-13 (41, 47), we found that stimulation of monocytes with IL-13 caused a dose-dependent and significant reduction in LPS-induced IL-1β concentration in cell culture supernatants (data not shown). Western analysis of 20 μg of cell lysate showed that at least part of the reduction is due to decreased processing of pro-IL-1β (Fig. 5). In unstimulated control cells, no IL-1β was detectable. But after LPS stimulation, there was detectable 31-kDa pro-IL-1β and also mature 17-kDa IL-1β, indicating that the proteolytic cleavage of IL-1β was occurring. However, pretreatment of the cells with IL-13 reduced the ratio of mature IL-1β to pro-IL-1β, which corresponds with the deficiency in active caspase 1 and hence the capacity for proteolytic cleavage of pro-IL-1β. Because IL-13 stimulation did not cause an accumulation of pro-IL-1β (the levels of pro-IL-1β were comparable regardless of IL-13 stimulation), it is possible that IL-13 also caused a decrease in IL-1β translation (we did not see down-regulation of IL-1β mRNA by microarray analysis), or that pro-IL-1β was being secreted (48).

FIGURE 5.

A, Western blot analysis of intracellular IL-1β protein. Monocytes were cultured as in Fig. 4, and cell lysates were prepared. Twenty micrograms of total protein were analyzed by Western blot for the presence of pro-IL-1β and its proteolytically cleaved mature form. LPS alone resulted in the expression of pro-IL-1β protein, and a significant proportion of this was processed to the mature form. Pretreatment with IL-13 did not prevent the expression of IL-1β but did reduce the proportion of mature IL-1β, suggesting a deficiency in proteolytic processing. B, Densitometric analysis of the bands shown in A, showing the clear reduction in the ratio of mature IL-1β to pro-IL-1β. Results are representative of two independent experiments.

FIGURE 5.

A, Western blot analysis of intracellular IL-1β protein. Monocytes were cultured as in Fig. 4, and cell lysates were prepared. Twenty micrograms of total protein were analyzed by Western blot for the presence of pro-IL-1β and its proteolytically cleaved mature form. LPS alone resulted in the expression of pro-IL-1β protein, and a significant proportion of this was processed to the mature form. Pretreatment with IL-13 did not prevent the expression of IL-1β but did reduce the proportion of mature IL-1β, suggesting a deficiency in proteolytic processing. B, Densitometric analysis of the bands shown in A, showing the clear reduction in the ratio of mature IL-1β to pro-IL-1β. Results are representative of two independent experiments.

Close modal

IL-13 is a prototypic Th2 cytokine mainly produced during the cellular or humoral immune response to parasitic and extracellular pathogens, and also during allergic reactions. In Mφ, IL-13 and IL-4 can induce an alternative activated phenotype characterized by the up-regulation of various molecules including mannose receptor (MRC1) and MHC class II (see Refs.2 and 3 for recent review).

In this study, we investigated the effects of IL-13 on monocytes using Affymetrix microarray technology. To our knowledge, this is the first transcriptome analysis performed on this cell population after stimulation with this Th2 cytokine. After 8-h stimulation with IL-13, 142 genes were identified with a statistically significant difference in expression, and a fold change of >2. Our microarray analysis included many of the known genes that are up- or down-regulated by IL-13.

IL-13Rs can also be expressed by other cell types including endothelial cells, smooth muscle cells, and fibroblasts. A recent paper by Jinnin et al. (49) investigated the transcriptional profile induced by IL-13 in fibroblasts. They saw significant regulation of genes such as α2(I) collagen, IL-16, and proteinase-activated receptor 1, but their analysis did not identify any of the genes described in our study. We have also conducted real-time PCR analysis of several of the genes listed in Table I in HUVECs. The majority of the genes were either absent, or expressed at several orders of magnitude lower than in monocytes. However, expression of CXCR4, IL1R1, and IL1R2 was detectable at significant levels, and IL-13 stimulation resulted in up-regulation of all three genes (data not shown). IL-13 will likely regulate its target genes in a cell type-specific manner, although control of some genes such as IL1R1/2 may be comparable in different cell populations. Because STAT6 is important for downstream signaling in both hemopoietic and nonhemopoietic cells in response to IL-13 or IL-4, other signaling events must contribute to the observed differences both between IL-13 and IL-4, and between cell types. For example, previous studies have investigated the transcriptional profile induced by IL-4 in murine Mφ (50, 51). Welch et al. (50) and Loke et al. (51) found IL-4 regulation of genes including MRC1 and FcγRIII, but there are distinct differences from our profile. Both groups demonstrated up-regulation of Ym1 and arginase, yet neither of these genes was found in our study. Such differences reflect the fact that IL-4 and IL-13 are highly similar yet different molecules.

Comparison with a recent paper by Jung et al. (52) on the IL-10-induced gene expression profile in monocytes is also interesting. Their results show that IL-10 regulates some of the genes identified in our profile. IL-10 and IL-13 have a comparable effect on the expression of IL-1Ra and IL13Rα1, but contrasting effects on CX3CR1, LILRB2, CD1e, and CD163. IL-10 bears some similarities to a Th2 cytokine and often has a similar expression pattern during an immune response, but these results demonstrate that, despite the apparent similarities, IL-10 and IL-13 have many opposing effects on monocytes.

GO data mining, and particularly EASE analysis, logically characterized the regulated genes as being involved with an inflammatory response. However, hierarchical clustering revealed a group of genes related to lipid metabolism and cholesterol biosynthesis. Combined with the observed up-regulation of MRC1, ALOX15, and PPARγ, plus the down-regulation of ABCA1, these genes may be of interest with respect to atherosclerosis and foam cell formation, where a role for IL-13 has been suggested but not categorically proven. Toucan analysis of TF binding sites suggests that the TF NF-Y may be involved with the regulation of these lipid metabolism genes; as well as regulating genes in the cholesterol biosynthesis pathway, this TF can also bind to the promoter for myeloperoxidase (MPO), another gene which has recently been implicated in atherosclerosis (53, 54).

Work by Huang et al. (43) has shown that IL-4 can up-regulate both PPARγ and ALOX15 in monocytes, and that ALOX15 can then generate ligands for PPARγ and hence coordinately mediate the induction of PPARγ-dependent genes. PPARγ induces the expression of MRC1 (55), and although there is currently no published data, it has been suggested that PPARγ may decrease the expression of CD163, another scavenger receptor that may be important in atherosclerosis (56, 57). Our results indicate that IL-13 may stimulate similar regulatory loops in monocytes, again with implications for disease (58).

As well as the effects on MRC1 and CD163, we also observed IL-13-dependent regulation of a number of other pattern recognition receptors. The up-regulation of CD1b/c/e may be of particular interest, because these molecules are involved with DC presentation of lipid and glycoprotein Ag to T cells (see Refs.59 and 60 for review). The early up-regulation of these genes in monocytes suggests that these cells may also be able to use CD1 for Ag presentation. Support for this suggestion is provided by the concomitant up-regulation of MRC1, which can endocytose Ag for eventual presentation by CD1b (13); maybe the other IL-13-regulated scavenger receptors can perform a similar function. Interestingly, TLR1 appears to be down-regulated by IL-13; this is a member of the TLR family, whose function is to recognize pathogens or their products and hence initiate innate immune responses (61, 62). A ligand for TLR1 alone has not been identified, but on heterodimerization with TLR2, the receptor complex can respond to microbial lipoproteins (63). IL-4 has been shown to down-regulate TLR2 in monocytes (64); this suggests that there may be functional significance for the decreased expression of TLR1 or TLR2 in response to Th2 cytokines. Regulation of these various pattern recognition receptors is currently being investigated in our laboratory.

The microarray analysis also revealed the up-regulation of SOCS1 and CISH; these molecules are members of the suppressors of cytokine signaling (SOCS) family, and are important for regulating the cellular response to cytokines. SOCS1 is induced by LPS and CpG in Mφ and can inhibit IFN-γ and IL-12 signaling (65). IL-4 and/or IL-13 up-regulate SOCS1 mRNA in a lung epithelial cell line and human keratinocytes, and SOCS1 can inhibit IL-4 signaling through the inhibition of JAKs (66, 67, 68, 69). CISH has been shown to negatively regulate IL-2 signaling in T cells, and it may also favor their differentiation into Th2 cells (65). Therefore, IL-13 up-regulation of SOCS1 could constitute a negative feedback loop, where SOCS1 expression inhibits further IL-13 signaling. In addition, SOCS1 or CISH could regulate the response to a variety of other cytokines and/or TLR ligands.

One of the most striking results from the microarray analysis was the regulation of genes involved with IL-1 signal transduction and biological activity. IL-1β is a fundamentally important proinflammatory cytokine that is produced during infection, injury, and other pathological situations, and that can act on almost every type of cell (70). Because IL-1 is such a potent inflammatory mediator, its effects must be tightly regulated to avoid toxicity. The up-regulation of IL-1RI, IL-1RII, and IL-1Ra is one of the hallmarks of the alternatively activated Mφ phenotype (2) and the increased expression of the IL-1 decoy receptor (IL-RII) and the receptor antagonist synergize to interfere with the effects of IL-1β on these cells (71). In this study, we observed the down-regulation of caspase 1 mRNA. This enzyme is responsible for the proteolytic cleavage of pro-IL-1β into its active mature form (44, 45). Our results show that pretreatment with IL-13 causes a reduction in caspase 1 activity and reduced processing of pro-IL-1β after LPS stimulation; this may contribute to the lower concentration of IL-1β seen in cell culture supernatants. The reduction in caspase 1 activity is expected to have similar effects on the processing of IL-18 (72). Of interest, real-time PCR analysis of MMP-9 showed a decrease in mRNA levels of this enzyme (although this was not seen by microarray analysis), which will be confirmed in future work. MMP-9 can also process pro-IL-1β to mature IL-1β in a caspase-independent pathway (73), suggesting a potential role for this enzyme in IL-1β regulation by IL-13. PPARγ has been shown to negatively regulate MMP-9 activity in Mφ (74). Moreover, PPARγ can inhibit the production of IL-1β as well (75). We also have some preliminary data that IL-13 can decrease the expression of Pellino 1. This protein is required for IL-1β-mediated signaling through IRAK1, IRAK4, and TRAF6; its down-regulation could therefore impede IL-1β signaling.

In conclusion, we have provided the first transcriptome analysis of human monocytes after stimulation with IL-13. Many characteristic markers of alternatively activated Mφ were seen in these cells, plus a variety of highly interesting novel IL-13-regulated genes. These included CD1, TLR1, and SOCS1, plus various components of the IL-1 system. Taken together, our microarray data outline the complex biological system required for the tight control of IL-1β production and response. IL-13 stimulation leads to a decrease in caspase 1 activity, which consequently limits the production of mature IL-1β. Meanwhile, increased expression of IL-1RII and IL-1Ra can negatively regulate the response to extracellular IL-1β. Clearly, IL-1β response and production is rigidly controlled in monocytes, and this will have a significant impact in the surrounding microenvironment; IL-13 will drive the alternative activation of monocyte/Mφ and the reduced response to IL-1β will potentiate this effect.

Table IIA.

Continues

GenBank Accession No.Gene SymbolGene DescriptionFold ChangeCluster
2 h8 h
Enzymes or metabolism      
 NM_000018 ACADVL Acyl-coenzyme A dehydrogenase, very long chain NC 2.3 1B 
 X02189 ADA Adenosine deaminase NC −4.0 
 AK000667 ADAM15 A disintegrin and metalloproteinase domain 15 (metargidin) NC 2.0 2A 
 NM_021778 ADAM28 A disintegrin and metalloproteinase domain 28 NC −9.2 
 AB015228 ALDH1A2 Aldehyde dehydrogenase 1 family, member A2 NC 4.3 1B 
 NM_001140 ALOX15 Arachidonate 15-lipoxygenase NC 9.8 1B 
 AI916249 AMPD2 Adenosine monophosphate deaminase 2 (isoform L) 2.8 2.1 2A 
 U34877 BLVRA Biliverdin reductase A 2.1 2.6 2A 
 U13699 CASP1 Caspase 1 (IL-1 β, convertase) −3.3 −4.6 
 NM_004267 CHST2 Carbohydrate (N-acetylglucosamine-6-O) sulfotransferase 2 −2.3 −2.0 
 NM_001814 CTSC Cathepsin C 5.7 7.5 2A 
 AI308863 CYBB Cytochrome b-245, β polypeptide (chronic granulomatous disease) −3.5 −2.6 
 NM_014762 DHCR24 24-dehydrocholesterol reductase NC 2.0 1A 
 L35594 ENPP2 Ectonucleotide pyrophosphatase/phosphodiesterase 2 (autotaxin) 3.5 6.5 2A 
 NM_000129 F13A1 Coagulation factor XIII, A1 polypeptide NC 10.6 2A 
 NM_001442 FABP4 Fatty acid binding protein 4, adipocyte NC 22.6 1B 
 AL512760 FADS1 Fatty acid desaturase 1 −9.2 2.1 1A 
 NM_004265 FADS2 Fatty acid desaturase 2 NC 2.3 1A 
 NM_000402 G6PD Glucose-6-phosphate dehydrogenase −2.3 2.0 1A 
 NM_002084 GPX3 Glutathione peroxidase 3 (plasma) NC 2.6 1B 
 AF155510 HPSE Heparanase 2.5 −4.3 
 NM_005525 HSD11B1 Hydroxysteroid (11 β) dehydrogenase 1 NC 7.0 1B 
 NM_013417 IARS Isoleucine-tRNA synthetase 2.5 2.1 2A 
 NM_000235 LIPA Lipase A, lysosomal acid, cholesterol esterase (Wolman disease) NC 5.3 1A 
 J02959 LTA4H Leukotriene A4 hydrolase NC −2.5 
 AA923354 MAOA Monoamine oxidase A 7.5 21.1 2A 
 NM_002450 MT1X Metallothionein 1X NC −2.0 
 L13974 NFE2 Nuclear factor (erythroid-derived 2), 45 kDa 18.3 2.8 2B 
 AF033026 PAPSS1 3′-phosphoadenosine 5′-phosphosulfate synthase 1 NC −2.1 
 NM_002627 PFKP Phosphofructokinase, platelet 2.6 2.5 2A 
 NM_014968 PITRM1 Pitrilysin metalloproteinase 1 3.2 2.3 2A 
 NM_002775 PRSS11 Protease, serine, 11 (IGF binding) NC −4.3 
 U93162 SC4MOL Sterol-C4-methyl oxidase-like −9.9 2.0 1A 
 NM_005668 SIAT8D Sialyltransferase 8D (α-2,8-polysialyltransferase) NC −2.5 
 BE742268 SORT1 Sortilin 1 NC 2.1 2A 
 AB022918 ST3GALVI α2,3-sialyltransferase −4.6 −2.6 
 NM_004613 TGM2 Transglutaminase 2 21.1 5.3 2B 
Signaling      
 AK026415 CHN2 Chimerin (chimaerin) 2 3.7 4.0 2A 
 NM_013324 CISH Cytokine inducible SH2-containing protein 8.0 4.9 2A 
 N36770 DUSP10 Dual specificity phosphatase 10 −2.6 −3.2 
 AK024456 FGD2 FGD1 family, member 2 2.1 3.0 2A 
 BC005147 FKBP1A FK506 binding protein 1A, 12 kDa 2.3 2.6 2A 
 Y19026 HOMER2 Homer homolog 2 (Drosophila2.8 3.2 2A 
 NM_007199 IRAK3 IL-1R-associated kinase 3 −1.6 −2.3 
 U77914 JAG1 Jagged 1 (Alagille syndrome) 16.0 4.9 2B 
 NM_021630 PDLIM2 PDZ and LIM domain 2 (mystique) NC 2.0 1B 
 NM_015869 PPARG Peroxisome proliferative-activated receptor γ 10.6 3.5 2A 
 AF027706 RIPK2 Receptor-interacting serine-threonine kinase 2 7.5 2.5 2B 
 AI992251 RPS6KA2 Ribosomal protein S6 kinase, 90 kDa, polypeptide 2 NC −2.5 
 U44403 SLA Src-like-adaptor 2.6 2.6 2A 
 AB005043 SOCS1 Suppressor of cytokine signaling 1 22.6 9.1 2A 
 BC000616 SWAP70 SWAP-70 protein −2.6 −2.6 
 NM_021732 VIP32 Vasopressin-induced transcript NC 2.0 2A 
Membrane trafficking      
 NM_004794 RAB33A RAB33A, member RAS oncogene family 8.6 2.0 2B 
 D42043 RAFTLIN Raft-linking protein NC 2.1 1B 
 AB020663 RC3 Rabconnectin-3 −2.8 −2.1 
 AL136924 RIN2 Ras and Rab interactor 2 NC −2.1 
 NM_000345 SNCA Synuclein, α (non-A4 component of amyloid precursor) −2.8 −2.6 
 NM_003165 STXBP1 Syntaxin binding protein 1 −1.7 2.5 1B 
 NM_004710 SYNGR2 Synaptogyrin 2 1.9 2.3 2A 
Transcription factors      
 NM_020183 ARNTL2 Aryl hydrocarbon receptor nuclear translocator-like 2 3.5 3.2 2A 
 NM_014038 BZW2 Basic leucine zipper and W2 domains 2 3.5 2.3 2A 
 AF052094 EPAS1 Endothelial PAS domain protein 1 2.8 3.7 2A 
 NM_005360 MAF V-maf musculoaponeurotic fibrosarcoma oncogene homolog (avian) 12.1 14.9 2A 
 NM_014112 TRPS1 Trichorhinophalangeal syndrome 1 NC −3.2 
     (Table continues
GenBank Accession No.Gene SymbolGene DescriptionFold ChangeCluster
2 h8 h
Enzymes or metabolism      
 NM_000018 ACADVL Acyl-coenzyme A dehydrogenase, very long chain NC 2.3 1B 
 X02189 ADA Adenosine deaminase NC −4.0 
 AK000667 ADAM15 A disintegrin and metalloproteinase domain 15 (metargidin) NC 2.0 2A 
 NM_021778 ADAM28 A disintegrin and metalloproteinase domain 28 NC −9.2 
 AB015228 ALDH1A2 Aldehyde dehydrogenase 1 family, member A2 NC 4.3 1B 
 NM_001140 ALOX15 Arachidonate 15-lipoxygenase NC 9.8 1B 
 AI916249 AMPD2 Adenosine monophosphate deaminase 2 (isoform L) 2.8 2.1 2A 
 U34877 BLVRA Biliverdin reductase A 2.1 2.6 2A 
 U13699 CASP1 Caspase 1 (IL-1 β, convertase) −3.3 −4.6 
 NM_004267 CHST2 Carbohydrate (N-acetylglucosamine-6-O) sulfotransferase 2 −2.3 −2.0 
 NM_001814 CTSC Cathepsin C 5.7 7.5 2A 
 AI308863 CYBB Cytochrome b-245, β polypeptide (chronic granulomatous disease) −3.5 −2.6 
 NM_014762 DHCR24 24-dehydrocholesterol reductase NC 2.0 1A 
 L35594 ENPP2 Ectonucleotide pyrophosphatase/phosphodiesterase 2 (autotaxin) 3.5 6.5 2A 
 NM_000129 F13A1 Coagulation factor XIII, A1 polypeptide NC 10.6 2A 
 NM_001442 FABP4 Fatty acid binding protein 4, adipocyte NC 22.6 1B 
 AL512760 FADS1 Fatty acid desaturase 1 −9.2 2.1 1A 
 NM_004265 FADS2 Fatty acid desaturase 2 NC 2.3 1A 
 NM_000402 G6PD Glucose-6-phosphate dehydrogenase −2.3 2.0 1A 
 NM_002084 GPX3 Glutathione peroxidase 3 (plasma) NC 2.6 1B 
 AF155510 HPSE Heparanase 2.5 −4.3 
 NM_005525 HSD11B1 Hydroxysteroid (11 β) dehydrogenase 1 NC 7.0 1B 
 NM_013417 IARS Isoleucine-tRNA synthetase 2.5 2.1 2A 
 NM_000235 LIPA Lipase A, lysosomal acid, cholesterol esterase (Wolman disease) NC 5.3 1A 
 J02959 LTA4H Leukotriene A4 hydrolase NC −2.5 
 AA923354 MAOA Monoamine oxidase A 7.5 21.1 2A 
 NM_002450 MT1X Metallothionein 1X NC −2.0 
 L13974 NFE2 Nuclear factor (erythroid-derived 2), 45 kDa 18.3 2.8 2B 
 AF033026 PAPSS1 3′-phosphoadenosine 5′-phosphosulfate synthase 1 NC −2.1 
 NM_002627 PFKP Phosphofructokinase, platelet 2.6 2.5 2A 
 NM_014968 PITRM1 Pitrilysin metalloproteinase 1 3.2 2.3 2A 
 NM_002775 PRSS11 Protease, serine, 11 (IGF binding) NC −4.3 
 U93162 SC4MOL Sterol-C4-methyl oxidase-like −9.9 2.0 1A 
 NM_005668 SIAT8D Sialyltransferase 8D (α-2,8-polysialyltransferase) NC −2.5 
 BE742268 SORT1 Sortilin 1 NC 2.1 2A 
 AB022918 ST3GALVI α2,3-sialyltransferase −4.6 −2.6 
 NM_004613 TGM2 Transglutaminase 2 21.1 5.3 2B 
Signaling      
 AK026415 CHN2 Chimerin (chimaerin) 2 3.7 4.0 2A 
 NM_013324 CISH Cytokine inducible SH2-containing protein 8.0 4.9 2A 
 N36770 DUSP10 Dual specificity phosphatase 10 −2.6 −3.2 
 AK024456 FGD2 FGD1 family, member 2 2.1 3.0 2A 
 BC005147 FKBP1A FK506 binding protein 1A, 12 kDa 2.3 2.6 2A 
 Y19026 HOMER2 Homer homolog 2 (Drosophila2.8 3.2 2A 
 NM_007199 IRAK3 IL-1R-associated kinase 3 −1.6 −2.3 
 U77914 JAG1 Jagged 1 (Alagille syndrome) 16.0 4.9 2B 
 NM_021630 PDLIM2 PDZ and LIM domain 2 (mystique) NC 2.0 1B 
 NM_015869 PPARG Peroxisome proliferative-activated receptor γ 10.6 3.5 2A 
 AF027706 RIPK2 Receptor-interacting serine-threonine kinase 2 7.5 2.5 2B 
 AI992251 RPS6KA2 Ribosomal protein S6 kinase, 90 kDa, polypeptide 2 NC −2.5 
 U44403 SLA Src-like-adaptor 2.6 2.6 2A 
 AB005043 SOCS1 Suppressor of cytokine signaling 1 22.6 9.1 2A 
 BC000616 SWAP70 SWAP-70 protein −2.6 −2.6 
 NM_021732 VIP32 Vasopressin-induced transcript NC 2.0 2A 
Membrane trafficking      
 NM_004794 RAB33A RAB33A, member RAS oncogene family 8.6 2.0 2B 
 D42043 RAFTLIN Raft-linking protein NC 2.1 1B 
 AB020663 RC3 Rabconnectin-3 −2.8 −2.1 
 AL136924 RIN2 Ras and Rab interactor 2 NC −2.1 
 NM_000345 SNCA Synuclein, α (non-A4 component of amyloid precursor) −2.8 −2.6 
 NM_003165 STXBP1 Syntaxin binding protein 1 −1.7 2.5 1B 
 NM_004710 SYNGR2 Synaptogyrin 2 1.9 2.3 2A 
Transcription factors      
 NM_020183 ARNTL2 Aryl hydrocarbon receptor nuclear translocator-like 2 3.5 3.2 2A 
 NM_014038 BZW2 Basic leucine zipper and W2 domains 2 3.5 2.3 2A 
 AF052094 EPAS1 Endothelial PAS domain protein 1 2.8 3.7 2A 
 NM_005360 MAF V-maf musculoaponeurotic fibrosarcoma oncogene homolog (avian) 12.1 14.9 2A 
 NM_014112 TRPS1 Trichorhinophalangeal syndrome 1 NC −3.2 
     (Table continues
Table IIB.

Continues

GenBank Accession No.Gene SymbolGene DescriptionFold ChangeCluster
2 h8 h
Related to extracellular matrix      
 NM_007267 EVER1 Epidermodysplasia verruciformis 1 2.3 2.5 2A 
 BF940043 NID Nidogen (enactin) NC −5.0 
 NM_003246 THBS1 Thrombospondin 1 −10.6 −5.0 
 NM_003256 TIMP4 Tissue inhibitor of metalloproteinase 4 4.3 2.3 2A 
Unknown      
 NM_020152 C21orf7 Chromosome 21 open reading frame 7 NC −2.6 
 NM_022102 C6orf79 Chromosome 6 open reading frame 79 2.6 2.3 2A 
 NM_014367 E2IG5 Growth and transformation-dependent protein 2.3 2.1 2A 
 BC003163 EEG1 Likely ortholog of mouse embryonic epithelial gene 1 NC −2.5 
 NM_018243 FLJ10849 Hypothetical protein FLJ10849 NC 2.5 1B 
 NM_018390 FLJ11323 Hypothetical protein FLJ11323 NC 2.5 1B 
 NM_017933 FLJ20701 Hypothetical protein FLJ20701 −3.3 −3.2 
 AF117234 FLOT1 Flotillin 1 NC −2.3 
 NM_002510 GPNMB Glycoprotein (transmembrane) nmb −4.6 −3.0 
 NM_014745 KIAA0233 KIAA0233 gene product NC 2.0 1B 
 AI962693 KIAA0555 KIAA0555 gene product NC −2.1 
 AA781143 LOC56926 Hypothetical protein from EUROIMAGE 2021883 NC 2.5 1B 
 NM_024300 MGC2217 Hypothetical protein MGC2217 3.7 2.8 2A 
 AL049435  Homo sapiens mRNA; cDNA DKFZp586B0220 −4.6 −4.0 
 U40053   −2.6 2.1 1A 
GenBank Accession No.Gene SymbolGene DescriptionFold ChangeCluster
2 h8 h
Related to extracellular matrix      
 NM_007267 EVER1 Epidermodysplasia verruciformis 1 2.3 2.5 2A 
 BF940043 NID Nidogen (enactin) NC −5.0 
 NM_003246 THBS1 Thrombospondin 1 −10.6 −5.0 
 NM_003256 TIMP4 Tissue inhibitor of metalloproteinase 4 4.3 2.3 2A 
Unknown      
 NM_020152 C21orf7 Chromosome 21 open reading frame 7 NC −2.6 
 NM_022102 C6orf79 Chromosome 6 open reading frame 79 2.6 2.3 2A 
 NM_014367 E2IG5 Growth and transformation-dependent protein 2.3 2.1 2A 
 BC003163 EEG1 Likely ortholog of mouse embryonic epithelial gene 1 NC −2.5 
 NM_018243 FLJ10849 Hypothetical protein FLJ10849 NC 2.5 1B 
 NM_018390 FLJ11323 Hypothetical protein FLJ11323 NC 2.5 1B 
 NM_017933 FLJ20701 Hypothetical protein FLJ20701 −3.3 −3.2 
 AF117234 FLOT1 Flotillin 1 NC −2.3 
 NM_002510 GPNMB Glycoprotein (transmembrane) nmb −4.6 −3.0 
 NM_014745 KIAA0233 KIAA0233 gene product NC 2.0 1B 
 AI962693 KIAA0555 KIAA0555 gene product NC −2.1 
 AA781143 LOC56926 Hypothetical protein from EUROIMAGE 2021883 NC 2.5 1B 
 NM_024300 MGC2217 Hypothetical protein MGC2217 3.7 2.8 2A 
 AL049435  Homo sapiens mRNA; cDNA DKFZp586B0220 −4.6 −4.0 
 U40053   −2.6 2.1 1A 
a

Analyzed using Affymetrix HG-U133A arrays as described in Materials and Methods. Each gene is given a representative GenBank accession number, common gene symbol, brief description, fold change at 8 h (relative to unstimulated cells; negative values indicate down regulation) plus the respective fold change at 2 h (NC, no change), and a cluster number which refers to the hierarchical clustering shown in Fig. 1.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1

C.J.S. was supported by a Marie Curie Fellowship from the European Community Human Potential Programme under Contract No. HPMF-CT-2001-01410. We also thank Associazione Italiana per la Ricerca sul Cancro and Ministero dell’Istruzione Università e Ricerca (cofin 2002) for financial support.

4

Abbreviations used in this paper: Mφ, macrophage; TF, transcription factor; Ct, cycle threshold; DC, dendritic cell; GO, Gene Ontology; EASE, Expression Analysis Systematic Explorer; FLICA, fluorochrome inhibitor of caspases.

1
Grage-Griebenow, E., H. D. Flad, M. Ernst.
2001
. Heterogeneity of human peripheral blood monocyte subsets.
J. Leukocyte Biol.
69
:
11
.
2
Gordon, S..
2003
. Alternative activation of macrophages.
Nat. Rev. Immunol.
3
:
23
.
3
Mosser, D. M..
2003
. The many faces of macrophage activation.
J. Leukocyte Biol.
73
:
209
.
4
Mantovani, A., S. Sozzani, M. Locati, P. Allavena, A. Sica.
2002
. Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes.
Trends Immunol.
23
:
549
.
5
Dalton, D. K., S. Pitts-Meek, S. Keshav, I. S. Figari, A. Bradley, T. A. Stewart.
1993
. Multiple defects of immune cell function in mice with disrupted interferon-γ genes.
Science
259
:
1739
.
6
MacMicking, J., Q. W. Xie, C. Nathan.
1997
. Nitric oxide and macrophage function.
Annu. Rev. Immunol.
15
:
323
.
7
Nathan, C. F., H. W. Murray, M. E. Wiebe, B. Y. Rubin.
1983
. Identification of interferon-γ as the lymphokine that activates human macrophage oxidative metabolism and antimicrobial activity.
J. Exp. Med.
158
:
670
.
8
Gerber, J. S., D. M. Mosser.
2001
. Reversing lipopolysaccharide toxicity by ligating the macrophage Fcγ receptors.
J. Immunol.
166
:
6861
.
9
Sutterwala, F. S., G. J. Noel, R. Clynes, D. M. Mosser.
1997
. Selective suppression of interleukin-12 induction after macrophage receptor ligation.
J. Exp. Med.
185
:
1977
.
10
Sutterwala, F. S., G. J. Noel, P. Salgame, D. M. Mosser, R. Clynes.
1998
. Reversal of proinflammatory responses by ligating the macrophage Fcγ receptor type I.
J. Exp. Med.
188
:
217
.
11
Anderson, C. F., D. M. Mosser.
2002
. A novel phenotype for an activated macrophage: the type 2 activated macrophage.
J. Leukocyte Biol.
72
:
101
.
12
Stein, M., S. Keshav, N. Harris, S. Gordon.
1992
. Interleukin 4 potently enhances murine macrophage mannose receptor activity: a marker of alternative immunologic macrophage activation.
J. Exp. Med.
176
:
287
.
13
Prigozy, T. I., P. A. Sieling, D. Clemens, P. L. Stewart, S. M. Behar, S. A. Porcelli, M. B. Brenner, R. L. Modlin, M. Kronenberg.
1997
. The mannose receptor delivers lipoglycan antigens to endosomes for presentation to T cells by CD1b molecules.
Immunity
6
:
187
.
14
Sallusto, F., M. Cella, C. Danieli, A. Lanzavecchia.
1995
. Dendritic cells use macropinocytosis and the mannose receptor to concentrate macromolecules in the major histocompatibility complex class II compartment: downregulation by cytokines and bacterial products.
J. Exp. Med.
182
:
389
.
15
Schebesch, C., V. Kodelja, C. Muller, N. Hakij, S. Bisson, C. E. Orfanos, S. Goerdt.
1997
. Alternatively activated macrophages actively inhibit proliferation of peripheral blood lymphocytes and CD4+ T cells in vitro.
Immunology
92
:
478
.
16
Mueller, T. D., J. L. Zhang, W. Sebald, A. Duschl.
2002
. Structure, binding, and antagonists in the IL-4/IL-13 receptor system.
Biochim. Biophys. Acta
1592
:
237
.
17
Hershey, G. K..
2003
. IL-13 receptors and signaling pathways: an evolving web.
J. Allergy Clin. Immunol.
111
:
677
.
18
Jordan, N. J., M. L. Watson, R. J. Williams, A. G. Roach, T. Yoshimura, J. Westwick.
1997
. Chemokine production by human vascular smooth muscle cells: modulation by IL-13.
Br. J. Pharmacol.
122
:
749
.
19
Sironi, M., F. L. Sciacca, C. Matteucci, M. Conni, A. Vecchi, S. Bernasconi, A. Minty, D. Caput, P. Ferrara, F. Colotta, et al
1994
. Regulation of endothelial and mesothelial cell function by interleukin-13: selective induction of vascular cell adhesion molecule-1 and amplification of interleukin-6 production.
Blood
84
:
1913
.
20
Folcik, V. A., R. Aamir, M. K. Cathcart.
1997
. Cytokine modulation of LDL oxidation by activated human monocytes.
Arterioscler. Thromb. Vasc. Biol.
17
:
1954
.
21
McKenzie, A. N., J. A. Culpepper, R. de Waal Malefyt, F. Briere, J. Punnonen, G. Aversa, A. Sato, W. Dang, B. G. Cocks, S. Menon, et al
1993
. Interleukin 13, a T-cell-derived cytokine that regulates human monocyte and B-cell function.
Proc. Natl. Acad. Sci. USA
90
:
3735
.
22
Bolstad, B. M., R. A. Irizarry, M. Astrand, T. P. Speed.
2003
. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.
Bioinformatics
19
:
185
.
23
Irizarry, R. A., B. M. Bolstad, F. Collin, L. M. Cope, B. Hobbs, and T. P. 2003. Speed: summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31:e15.
24
Reiner, A., D. Yekutieli, Y. Benjamini.
2003
. Identifying differentially expressed genes using false discovery rate controlling procedures.
Bioinformatics
19
:
368
.
25
Ashburner, M., C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, K. Dolinski, S. S. Dwight, J. T. Eppig, et al
2000
. Gene ontology: tool for the unification of biology: The Gene Ontology Consortium.
Nat. Genet.
25
:
25
.
26
Hosack, D. A., G. Dennis, Jr, B. T. Sherman, H. C. Lane, R. A. Lempicki.
2003
. Identifying biological themes within lists of genes with EASE.
Genome Biol.
4
:
R70
.
27
Dennis, G., Jr, B. T. Sherman, D. A. Hosack, J. Yang, W. Gao, H. C. Lane, R. A. Lempicki.
2003
. DAVID: Database for Annotation, Visualization, and Integrated Discovery.
Genome Biol.
4
:
3
.
28
Aerts, S., G. Thijs, B. Coessens, M. Staes, Y. Moreau, B. De Moor.
2003
. Toucan: deciphering the cis-regulatory logic of coregulated genes.
Nucleic Acids Res.
31
:
1753
.
29
Wingender, E., X. Chen, E. Fricke, R. Geffers, R. Hehl, I. Liebich, M. Krull, V. Matys, H. Michael, R. Ohnhauser, et al
2001
. The TRANSFAC system on gene expression regulation.
Nucleic Acids Res.
29
:
281
.
30
Rozen, R., H. J. Skaletsky.
2000
. Primer 3 on the WWW for general users and for biologist programmers. S. Krawetz, Jr, and S. Misener, Jr, eds.
Bioinformatics Methods and Protocols: Methods in Molecular Biology
365
. Humana, Totowa, NJ.
31
Bonecchi, R., S. Sozzani, J. T. Stine, W. Luini, G. D’Amico, P. Allavena, D. Chantry, A. Mantovani.
1998
. Divergent effects of interleukin-4 and interferon-γ on macrophage-derived chemokine production: an amplification circuit of polarized T helper 2 responses.
Blood
92
:
2668
.
32
Colotta, F., S. Saccani, J. G. Giri, S. K. Dower, J. E. Sims, M. Introna, A. Mantovani.
1996
. Regulated expression and release of the IL-1 decoy receptor in human mononuclear phagocytes.
J. Immunol.
156
:
2534
.
33
Muzio, M., F. Re, M. Sironi, N. Polentarutti, A. Minty, D. Caput, P. Ferrara, A. Mantovani, F. Colotta.
1994
. Interleukin-13 induces the production of interleukin-1 receptor antagonist (IL-1ra) and the expression of the mRNA for the intracellular (keratinocyte) form of IL-1ra in human myelomonocytic cells.
Blood
83
:
1738
.
34
Fritsche, J., A. Muller, M. Hausmann, G. Rogler, R. Andreesen, M. Kreutz.
2003
. Inverse regulation of the ADAM-family members, decysin and MADDAM/ADAM19 during monocyte differentiation.
Immunology
110
:
450
.
35
Brandon, C., L. M. Eisenberg, C. A. Eisenberg.
2000
. WNT signaling modulates the diversification of hematopoietic cells.
Blood
96
:
4132
.
36
Vainio, S., E. Ikonen.
2003
. Macrophage cholesterol transport: a critical player in foam cell formation.
Ann. Med.
35
:
146
.
37
Jackson, S. M., J. Ericsson, T. F. Osborne, P. A. Edwards.
1995
. NF-Y has a novel role in sterol-dependent transcription of two cholesterogenic genes.
J. Biol. Chem.
270
:
21445
.
38
Kim, J. H., J. N. Lee, Y. K. Paik.
2001
. Cholesterol biosynthesis from lanosterol: a concerted role for Sp1 and NF-Y-binding sites for sterol-mediated regulation of rat 7-dehydrocholesterol reductase gene expression.
J. Biol. Chem.
276
:
18153
.
39
Bonecchi, R., F. Facchetti, S. Dusi, W. Luini, D. Lissandrini, M. Simmelink, M. Locati, S. Bernasconi, P. Allavena, E. Brandt, et al
2000
. Induction of functional IL-8 receptors by IL-4 and IL-13 in human monocytes.
J. Immunol.
164
:
3862
.
40
Leveque, C., S. Grafte, J. Paysant, A. Soutif, B. Lenormand, M. Vasse, C. Soria, J. P. Vannier.
1998
. Regulation of interleukin 3 receptor α chain (IL-3Rα) on human monocytes by interleukin (IL)-4, IL-10, IL-13, and transforming growth factor β (TGF-β).
Cytokine
10
:
487
.
41
de Waal Malefyt, R., C. G. Figdor, R. Huijbens, S. Mohan-Peterson, B. Bennett, J. Culpepper, W. Dang, G. Zurawski, J. E. de Vries.
1993
. Effects of IL-13 on phenotype, cytokine production, and cytotoxic function of human monocytes: comparison with IL-4 and modulation by IFN-γ or IL-10.
J. Immunol.
151
:
6370
.
42
Richard, M., P. Veilleux, M. Rouleau, R. Paquin, A. D. Beaulieu.
2002
. The expression pattern of the ITIM-bearing lectin CLECSF6 in neutrophils suggests a key role in the control of inflammation.
J. Leukocyte Biol.
71
:
871
.
43
Huang, J. T., J. S. Welch, M. Ricote, C. J. Binder, T. M. Willson, C. Kelly, J. L. Witztum, C. D. Funk, D. Conrad, C. K. Glass.
1999
. Interleukin-4-dependent production of PPAR-γ ligands in macrophages by 12/15-lipoxygenase.
Nature
400
:
378
.
44
Cerretti, D. P., C. J. Kozlosky, B. Mosley, N. Nelson, K. Van Ness, T. A. Greenstreet, C. J. March, S. R. Kronheim, T. Druck, L. A. Cannizzaro, et al
1992
. Molecular cloning of the interleukin-1β converting enzyme.
Science
256
:
97
.
45
Thornberry, N. A., H. G. Bull, J. R. Calaycay, K. T. Chapman, A. D. Howard, M. J. Kostura, D. K. Miller, S. M. Molineaux, J. R. Weidner, J. Aunins, et al
1992
. A novel heterodimeric cysteine protease is required for interleukin-1β processing in monocytes.
Nature
356
:
768
.
46
Burns, K., F. Martinon, J. Tschopp.
2003
. New insights into the mechanism of IL-1β maturation.
Curr. Opin. Immunol.
15
:
26
.
47
Vannier, E., L. C. Miller, C. A. Dinarello.
1992
. Coordinated antiinflammatory effects of interleukin 4: interleukin 4 suppresses interleukin 1 production but up-regulates gene expression and synthesis of interleukin 1 receptor antagonist.
Proc. Natl. Acad. Sci. USA
89
:
4076
.
48
Chin, J., M. J. Kostura.
1993
. Dissociation of IL-1β synthesis and secretion in human blood monocytes stimulated with bacterial cell wall products.
J. Immunol.
151
:
5574
.
49
Jinnin, M., H. Ihn, K. Yamane, K. Tamaki.
2004
. Interleukin-13 stimulates the transcription of the human α2(I) collagen gene in human dermal fibroblasts.
J. Biol. Chem.
279
:
41783
.
50
Welch, J. S., L. Escoubet-Lozach, D. B. Sykes, K. Liddiard, D. R. Greaves, C. K. Glass.
2002
. TH2 cytokines and allergic challenge induce Ym1 expression in macrophages by a STAT6-dependent mechanism.
J. Biol. Chem.
277
:
42821
.
51
Loke, P., M. G. Nair, J. Parkinson, D. Guiliano, M. Blaxter, J. E. Allen.
2002
. IL-4 dependent alternatively-activated macrophages have a distinctive in vivo gene expression phenotype.
BMC Immunol.
3
:
7
.
52
Jung, M., R. Sabat, J. Kratzschmar, H. Seidel, K. Wolk, C. Schonbein, S. Schutt, M. Friedrich, W. D. Docke, K. Asadullah, et al
2004
. Expression profiling of IL-10-regulated genes in human monocytes and peripheral blood mononuclear cells from psoriatic patients during IL-10 therapy.
Eur. J. Immunol.
34
:
481
.
53
Orita, T., K. Shimozaki, H. Murakami, S. Nagata.
1997
. Binding of NF-Y transcription factor to one of the cis-elements in the myeloperoxidase gene promoter that responds to granulocyte colony-stimulating factor.
J. Biol. Chem.
272
:
23216
.
54
Thukkani, A. K., C. J. Albert, K. R. Wildsmith, M. C. Messner, B. D. Martinson, F. F. Hsu, D. A. Ford.
2003
. Myeloperoxidase-derived reactive chlorinating species from human monocytes target plasmalogens in low density lipoprotein.
J. Biol. Chem.
278
:
36365
.
55
Coste, A., M. Dubourdeau, M. D. Linas, S. Cassaing, J. C. Lepert, P. Balard, S. Chalmeton, J. Bernad, C. Orfila, J. P. Seguela, B. Pipy.
2003
. PPARγ promotes mannose receptor gene expression in murine macrophages and contributes to the induction of this receptor by IL-13.
Immunity
19
:
329
.
56
Ratcliffe, N. R., S. M. Kennedy, P. M. Morganelli.
2001
. Immunocytochemical detection of Fcγ receptors in human atherosclerotic lesions.
Immunol. Lett.
77
:
169
.
57
Ritter, M., C. Buechler, M. Kapinsky, G. Schmitz.
2001
. Interaction of CD163 with the regulatory subunit of casein kinase II (CKII) and dependence of CD163 signaling on CKII and protein kinase C.
Eur. J. Immunol.
31
:
999
.
58
Ricote, M., J. T. Huang, J. S. Welch, C. K. Glass.
1999
. The peroxisome proliferator-activated receptor (PPARγ) as a regulator of monocyte/macrophage function.
J. Leukocyte Biol.
66
:
733
.
59
Dutronc, Y., S. A. Porcelli.
2002
. The CD1 family and T cell recognition of lipid antigens.
Tissue Antigens
60
:
337
.
60
Moody, D. B., S. A. Porcelli.
2003
. Intracellular pathways of CD1 antigen presentation.
Nat. Rev. Immunol.
3
:
11
.
61
Pasare, C., R. Medzhitov.
2003
. Toll-like receptors: balancing host resistance with immune tolerance.
Curr. Opin. Immunol.
15
:
677
.
62
Takeuchi, O., S. Akira.
2002
. Genetic approaches to the study of Toll-like receptor function.
Microbes Infect.
4
:
887
.
63
Takeuchi, O., S. Sato, T. Horiuchi, K. Hoshino, K. Takeda, Z. Dong, R. L. Modlin, S. Akira.
2002
. Cutting edge: role of Toll-like receptor 1 in mediating immune response to microbial lipoproteins.
J. Immunol.
169
:
10
.
64
Flo, T. H., O. Halaas, S. Torp, L. Ryan, E. Lien, B. Dybdahl, A. Sundan, T. Espevik.
2001
. Differential expression of Toll-like receptor 2 in human cells.
J. Leukocyte Biol.
69
:
474
.
65
Kubo, M., T. Hanada, A. Yoshimura.
2003
. Suppressors of cytokine signaling and immunity.
Nat. Immunol.
4
:
1169
.
66
Federici, M., M. L. Giustizieri, C. Scarponi, G. Girolomoni, C. Albanesi.
2002
. Impaired IFN-γ-dependent inflammatory responses in human keratinocytes overexpressing the suppressor of cytokine signaling 1.
J. Immunol.
169
:
434
.
67
Haque, S. J., P. C. Harbor, B. R. Williams.
2000
. Identification of critical residues required for suppressor of cytokine signaling-specific regulation of interleukin-4 signaling.
J. Biol. Chem.
275
:
26500
.
68
Hebenstreit, D., P. Luft, A. Schmiedlechner, G. Regl, A. M. Frischauf, F. Aberger, A. Duschl, J. Horejs-Hoeck.
2003
. IL-4 and IL-13 induce SOCS-1 gene expression in A549 cells by three functional STAT6-binding motifs located upstream of the transcription initiation site.
J. Immunol.
171
:
5901
.
69
Jiang, H., M. B. Harris, P. Rothman.
2000
. IL-4/IL-13 signaling beyond JAK/STAT.
J. Allergy Clin. Immunol.
105
:
1063
.
70
Dinarello, C. A..
1996
. Biologic basis for interleukin-1 in disease.
Blood
87
:
2095
.
71
Mantovani, A., M. Locati, A. Vecchi, S. Sozzani, P. Allavena.
2001
. Decoy receptors: a strategy to regulate inflammatory cytokines and chemokines.
Trends Immunol.
22
:
328
.
72
Akita, K., T. Ohtsuki, Y. Nukada, T. Tanimoto, M. Namba, T. Okura, R. Takakura-Yamamoto, K. Torigoe, Y. Gu, M. S. Su, et al
1997
. Involvement of caspase-1 and caspase-3 in the production and processing of mature human interleukin 18 in monocytic THP.1 cells.
J. Biol. Chem.
272
:
26595
.
73
Schonbeck, U., F. Mach, P. Libby.
1998
. Generation of biologically active IL-1β by matrix metalloproteinases: a novel caspase-1-independent pathway of IL-1β processing.
J. Immunol.
161
:
3340
.
74
Marx, N., G. Sukhova, C. Murphy, P. Libby, J. Plutzky.
1998
. Macrophages in human atheroma contain PPARγ: differentiation-dependent peroxisomal proliferator-activated receptor γ (PPARγ) expression and reduction of MMP-9 activity through PPARγ activation in mononuclear phagocytes in vitro.
Am. J. Pathol.
153
:
17
.
75
Jiang, C., A. T. Ting, B. Seed.
1998
. PPAR-γ agonists inhibit production of monocyte inflammatory cytokines.
Nature
391
:
82
.
76
Fraticelli, P., M. Sironi, G. Bianchi, D. D’Ambrosio, C. Albanesi, A. Stoppacciaro, M. Chieppa, P. Allavena, L. Ruco, G. Girolomoni, et al
2001
. Fractalkine (CX3CL1) as an amplification circuit of polarized Th1 responses.
J. Clin. Invest.
107
:
1173
.