Kawasaki disease (KD) is an acute vasculitis of infants and young children, preferentially affecting the coronary arteries. Intravenous infusion of high dose Ig (IVIG) effectively reduces systemic inflammation and prevents coronary artery lesions in KD. To investigate the mechanisms underlying the therapeutic effects of IVIG, we examined gene expression profiles of PBMC and purified monocytes obtained from acute patients before and after IVIG therapy. The results suggest that IVIG suppresses activated monocytes and macrophages by altering various functional aspects of the genes of KD patients. Among the 18 commonly decreased transcripts in both PBMC and purified monocytes, we selected six genes, FCGR1A, FCGR3A, CCR2, ADM, S100A9, and S100A12, and confirmed the microarray results by real-time RT-PCR. Moreover, the expressions of FcγRI and FcγRIII on monocytes were reduced after IVIG. Plasma S100A8/A9 heterocomplex, but not S100A9, levels were elevated in patients with acute KD compared with those in febrile controls. Furthermore, S100A8/A9 was rapidly down-regulated in response to IVIG therapy. Persistent elevation of S100A8/A9 after IVIG was found in patients who later developed coronary aneurysms. These results indicate that the effects of IVIG in KD may be mediated by suppression of an array of immune activation genes in monocytes, including those activating FcγRs and the S100A8/A9 heterocomplex.

Kawasaki disease (KD)3 is an acute systemic vasculitis that primarily affects infants and young children (1, 2). Although KD is, in most cases, a self-limited illness, resolving within a few weeks after fever onset, it preferentially affects coronary arteries, and without appropriate intervention, 15–25% of patients will develop coronary aneurysms or dilatation (3, 4). Intravenous infusion of high dose Ig (IVIG) was demonstrated to effectively reduce systemic inflammation and the incidence of coronary artery lesions (5, 6). However, the precise mechanisms underlying the effects of IVIG therapy in KD are unknown. Moreover, ∼15% of KD patients are not responsive to this therapy and must be given additional IVIG or immunosuppressive regimens, such as methylprednisone pulse therapy (7, 8). Because the incidence of KD in the Japanese population has increased from 88 to 140 per 100,000 children under 5 years of age in the 10 years since 1994, and the proportion of patients treated with IVIG now exceeds 85% in Japan (9), it is imperative that we clarify the mechanisms of action of IVIG therapy in KD.

IVIG therapy was first introduced in children with idiopathic thrombocytopenic purpura (ITP) by Imbach et al. in 1981 (10). Since then, it has been used for the treatment of various autoimmune diseases, such as vasculitis, Guillain-Barre syndrome, and dermatomyositis (11, 12, 13). In KD patients, IVIG was first reported by Furusho et al. in 1984 (5) to effectively reduce the incidence of coronary artery lesions. Subsequently, a single high dose Ig (2 g/kg body weight) infusion was shown to be more effective in reducing inflammation and fever than administration of the same dose divided over several days (6). In responsive patients, the serum levels of a variety of inflammatory mediators, such as cytokines (IL-1β, IL-6, and TNF-α) and chemokines (MCP-1, IL-8, and MIP-1) as well as acute inflammatory proteins (C-reactive peptide (CRP) and haptoglobin) are reportedly decreased after IVIG therapy (14, 15, 16). Spontaneous Ig synthesis by PBMC was also reported to be reduced after IVIG (17). These findings indicate that the effects of IVIG in KD are mediated mainly by robust suppression of activated immune cells in the peripheral circulation. Ichiyama recently reported that IVIG preparation inhibited TNF-α-induced NF-κB activation in cultured monocytic cells (18). However, which cell population is directly affected by IVIG remains to be clarified. Whether the suppressive effect is mediated though direct binding of Ig to cell surface receptors or through the neutralization or blockade of cytokine receptor pathways is also unknown.

With the development of DNA microarray technologies and functional genomics, we can now monitor the levels of >20,000 transcripts from a limited number of immune cells, allowing study of complex biological responses occurring in affected children (19). This method appears to be especially useful for investigating the unknown molecular phenomena associated with the administration of certain pharmaceutical agents, such as IVIG. In this study we attempted to analyze gene expression profiles in PBMC and purified monocytes to reveal which cell population and which transcripts are more affected by IVIG therapy in KD patients. We also investigated whether changes in gene expression are related to the therapeutic effects of IVIG and the clinical course of KD in our patients.

We studied 46 Japanese acute KD patients (age range, 2–76 mo; median, 19.5 mo) between April 2002 and March 2004. Eight patients (four boys and four girls) were treated at Hachiouji Metropolitan Children’s Hospital, 15 patients (eight boys and seven girls) were treated at Chiba University Hospitals, and 23 patients (10 boys and 13 girls) were treated at Kaihin-Chiba Municipal Hospital. All patients were diagnosed according to the guidelines established by the Kawasaki disease research committee in Japan. All patients were given IVIG therapy (1.0 g/kg for 1–2 days or 400 mg/kg for 4–6 days) and oral aspirin (10–30 mg/kg daily). Abnormal cardiac function and coronary artery lesions were monitored by two-dimensional echocardiography during the acute and convalescent phases of the disease. Two patients had transient mitral valve regurgitation and tricuspid valve regurgitation before IVIG, and four patients (8.3%) developed coronary aneurysms 1 mo after onset of the disease.

Venous blood was drawn from each patient before IVIG treatment (2–9 d after the onset of fever, median, 5 d) and within 7 days after completion of IVIG therapy (7–19 d after fever onset, median, 12 d). Twenty control blood samples were obtained from age-matched control patients (10 boys and 10 girls; age range, 1–78 mo; median, 10.5 mo) who had been febrile (body temperature >38°C) for at least 3 d. Their clinical diagnoses were acute upper respiratory infection (10), toxic shock syndrome-like exanthematous disease (3), cervical lymphadenitis (2), staphylococcal scalded skin syndrome (2), pneumonia (2), and enterocolitis (1). Informed consent was obtained from the patients’ parents according to the guidelines of each medical center.

Human PBMC were isolated by centrifugation on a Ficoll-Paque Plus (Amersham Biosciences) density gradient. Peripheral blood monocytes or T cells were separated from heparinized venous blood using a RosetteSep Monocyte or T Cell Enrichment Cocktail (StemCell Technologies) according to the manufacturer’s instructions (20). Briefly, 2 ml of heparinized blood was mixed with 20 μl of 100 mM EDTA and 100 μl of RosetteSep mixture containing Abs to human CD2, CD3, CD8, CD19, CD56, and CD66b for monocytes or to human CD16, CD19, CD36, and CD56 for T cells. After incubation for 20 min at room temperature, the sample was diluted with an equal volume of PBS containing 2% FBS and 1 mM EDTA and was layered on top of 4 ml of Ficoll-Paque. The tubes were then centrifuged at 2000 rpm at room temperature for 20 min. The interface between plasma and Ficoll-Paque was collected, washed, and stored in liquid nitrogen until RNA extraction. The CD14+ monocytes and CD3+ T cells typically represented ∼90% and 93.5%, respectively, of the total cells on flow cytometric analysis after these enrichment procedures.

Total RNA was isolated from the PBMC or monocyte-enriched fraction of PBMC using ISOGEN (Wako Pure Chemical Industries) according to the manufacturer’s instructions. Gene expression was examined using the human genome U133A probe array (GeneChip; Affymetrix), which contains the oligonucleotide probe set for 22,283 full-length genes and expressed sequence tags, according to the manufacturer’s protocol, and previous reports (21, 22). Five micrograms of total RNA from PBMC or 150 ng of total RNA from monocytes was used to synthesize double-stranded cDNA. The cDNA was next subjected to in vitro transcription in the presence of biotinylated nucleoside triphosphates. In the assay of monocyte RNA, two cycles of cDNA synthesis and in vitro transcription reactions were conducted to amplify target sequences. The biotinylated cRNA was hybridized with a U133A probe array for 16 h at 45°C, and the hybridized biotinylated cRNA was stained with streptavidin-PE (Molecular Probes) and then scanned with a Gene Array Scanner (Hewlett-Packard). The fluorescence intensity of each probe was quantified using a computer program, GeneChip Analysis Suite 5.0 (Affymetrix). The expression level of a single mRNA was determined as the average fluorescence intensity among the intensities obtained by 20 pairs (perfectly matched and single nucleotide-mismatched) of probes consisting of 25-mer oligonucleotides. The level of gene expression was determined as the average difference (AD) using GeneChip Analysis Suite 5.0. In this program, if the intensities of mismatched probes were high, gene expression was judged to be absent even if a high AD value was obtained from that particular gene. Under these conditions, we confirmed that the expression levels of genes in the same cells, analyzed twice, showed a statistically significant correlation (r = 0.997). The results of the GeneChip Analysis can be found on our web site at 〈www.nch.go.jp/imal/GeneChip/KAWASAKI.htm〉.

The data were further analyzed with GeneSpring software version 6.1 (Silicon Genetics). To normalize the staining intensity variations among the chips, the AD values for all genes on a given chip were divided by the median AD of all measurements on that chip. To eliminate changes within the range of background noise and to select the most differentially expressed genes, only probes whose AD values were judged to be present by GeneChip Analysis Suite 5.0 (Affymetrix) in at least two of four chips were included in the analysis. Hierarchical clustering analysis with standard correlation was used to identify gene clusters. The separation ratio was set at 0.5.

Total RNA was reverse transcribed to cDNA using SuperScript III reverse transcriptase (Invitrogen Life Technologies) and random hexamers (Amersham Biosciences). The PCR primers and probes were purchased from Applied Biosystems (Assays-on-Demand; Gene Expression Products) for GAPDH (assay no. Hs99999905), adrenomedullin (ADM) (Hs00181605), S100 calcium-binding protein family (S100) A8 (Hs00374264), and S100A12 (Hs00194525). The primers and probes for S100A9, CCR2, FCGR1A, FCGR3A, IL-6, IL-8, IL-10, and TNF-α were designed based on sequences from GenBank. Primer sequences were as follows: S100A9 forward primer, 5′-CCGTGGGCATCATGTTGAC-3′; S100A9 reverse primer, 5′-GGAAGGTGTTGATGATGGTCTCTA-3′; CCR2 forward primer, 5′-GCGTTTAATCACATTCGAGTGTTT-3′; CCR2 reverse primer, 5′-CCACTGGCAAATTAGGGAACAA-3′; FCGR1A forward primer, 5′-GGTTCTTGACAACTCTGCTCCTTT-3′; FCGR1A reverse primer, 5′-TTGGAACACGCTGACCCAT-3′; FCGR3A forward primer, 5′-ATTGACGCTGCCACAGTCAAC-3′; FCGR3A reverse primer, 5′-AGCCAGCCGATATGGACTTCT-3′; IL-6 forward primer, 5′-CCAGTACCCCCAGGAGAAGAT-3′; IL-6 reverse primer, 5′-CGTTCTGAAGAGGTGAGTGGC-3′; IL-8 forward primer, 5′-CACTGCGCCAACACAGAAATTA-3′; IL-8 reverse primer, 5′-ACTTCTCCACAACCCTCTGCAC-3′; IL-10 forward primer, 5′-TACGGCGCTGTCATCGATT-3′; IL-10 reverse primer, 5′-GGCATTCTTCACCTGCTCCA-3′; TNF-α forward primer, 5′-CCCTGGTATGAGCCCATCTATC-3′; and TNF-α reverse primer, 5′-AAAGTAGACCTGCCCAGACTCG-3′. PCR was conducted using the ABI 7700 sequence detector system (Applied Biosystems) in a 25-μl reaction mixture containing 12.5 μl of TaqMan Universal PCR Master Mix (Applied Biosystems), 1.25 μl of 20× Assays-on-Demand Gene Expression Assay Mixture (Applied Biosystems), or 1.25 μl of a mixture of forward and reverse primers (4.0 μM each) and FAM-labeled probe (2.0 μM), and 11.25 μl of cDNA diluted in RNase-free H2O. Samples were preincubated for 10 min at 95°C, then subjected to 40 cycles of amplification at 95°C for 15 s for denaturing and at 60°C for 1 min for annealing-extension. The expression of each target cDNA relative to GAPDH was calculated using a comparative Tc method described in the User Bulletin 2 provided by the manufacturer (Applied Biosystems) and was determined for each sample.

PBMC were suspended in staining solution consisting of PBS, 5% FCS, 0.02% sodium azide, and 1 mg/ml human IgG (Mitsubishi Pharma). The cells were incubated with one of the mAbs, 3G8, an Ab to human CD16, CIKM5, an Ab to human CD32, 10.1, an Ab to human CD64 (all from Caltag Laboratories), or 679.1Mc7, a mouse IgG1 isotype control (Beckman Coulter), followed by incubation with FITC-conjugated rat anti-mouse IgG1 mAb (BD Biosciences) and PE-anti-CD14 (Beckman Coulter). Fluorescence intensity was analyzed with a FACScan flow cytometer (BD Biosciences) and CellQuest software (BD Biosciences).

ELISA was performed using MRP8/14 ELISA (Buhlmann Laboratories) and MRP14 ELISA (Chemicon International) kits according to the manufacturer’s instructions. Heparinized test plasma was diluted 1/200 for the MRP8/14 kit, and 1/5 for the MRP14 kit in assay buffer, and 100 μl of each dilution was applied to a 96-well plate in duplicate. The absorbance was read at 450 nm in a microplate reader, and the protein concentration was calculated using Microplate Manager III software (Bio-Rad).

For GeneChip microarray data, the two-tailed paired t test was performed using normalized AD values with GeneSpring software version 6.1 (Silicon Genetics). For real-time RT-PCR, flow cytometry, and ELISA data, the two-tailed paired t test was used to compare patients’ samples obtained before vs after IVIG therapy, and one-factor ANOVA and the Scheffé F test as a post-hoc test were used to compare pre-IVIG patients with KD and control patients. Correlations between S100A8/A9 and S100A9 plasma levels and the laboratory data were assessed using Spearman’s rank test after cell counts had been logarithmically transformed. A value of p < 0.05 was considered statistically significant.

We first examined the gene expression profiles of PBMC obtained from four KD patients before and after IVIG therapy. The demographic and laboratory data of patients at the time of blood drawing are summarized in Tables I and II. By using the Human Genome U133A probe array, which contains the oligonucleotide probe set for 22,283 transcripts, 509 transcripts were found to have significantly changed expression levels after IVIG therapy (p = 0.05). Among them, four genes showed expression to be more than double the pre-IVIG levels, and 85 genes showed expression to be less than half the pre-IVIG levels after IVIG therapy (Fig. 1,A). In total, 75 differentially expressed genes were classified according to their cellular functions (Table III). Fourteen genes were excluded from Table II because their functions are currently unknown. Remarkably, most of the differentially expressed genes were down-regulated after IVIG. Among these down-regulated genes, cell surface receptors formed the largest functional group, most of which were mainly expressed in monocytes and macrophages. For example, FCGR1A, FCGR2A, FCGR3A, and formyl peptide receptor 1 are receptors that function in phagocytosis and transduce stimulatory signals to monocytes. The receptors of chemokines and growth factors for monocytes and macrophages, such as the CSF-1 receptor, the CSF-2 receptor, and CCR2, were also down-regulated after IVIG therapy. Among the down-regulated genes of secreted proteins, there was a preponderance of monocyte-derived molecules, such as ADM, S100A8, S100A9, S100A12, and endothelial cell growth factor 1. Based on these findings, we decided to determine whether the down-regulation of monocyte-related gene expressions was due to a decreased number of monocytes among PBMC from patients or to decreased mRNA synthesis in individual monocyte after IVIG. As an approach to this question, we examined gene expression profiles in the monocyte fraction purified from patients’ PBMC.

Table I.

Demographic data of patients

PBMC Array PatientsMonocyte Array PatientsOther Patients
Age (mo after birth) 17–36 (median, 26) 3–25 (median, 22) 2–76 (median, 16) 
Sex (male, female) 3, 1 1, 3 18, 20 
Blood drawn pre-IVIG (days after onset) 3–5 (median, 4) 5–9 (median, 6) 2–9 (median, 5) 
Blood drawn post-IVIG (days after onset) 8–14 (median, 12) 8–18 (median, 15) 7–18 (median, 13) 
Cardiac involvement (positive patients) 
Neutrophil count/mm3 (pre, post) 9,946 ± 3,792, 3,445 ± 763 11,203 ± 4,779, 3,322 ± 2,736 9,761 ± 3,353, 4,623 ± 5,110 
p valuea 0.04 0.02 0.002 
Monocyte count/mm3 (pre, post) 862 ± 540, 574 ± 290 1,434 ± 849, 818 ± 759 856 ± 700, 621 ± 587 
p valuea 0.42 0.16 0.06 
Lymphocyte count/mm3 (pre, post) 4,580 ± 735, 5,222 ± 3,055 5,380 ± 2,127, 5,988 ± 2,556 3,469 ± 1,885, 4,965 ± 2,361 
p valuea 0.72 0.75 0.001 
C reactive protein (mg/dl; pre, post) 10.4 ± 4.2, 0.8 ± 0.2 7.5 ± 3.4, 1.0 ± 0.6 10.1 ± 5.1, 1.9 ± 3.0 
p valuea 0.02 0.03 <0.001 
PBMC Array PatientsMonocyte Array PatientsOther Patients
Age (mo after birth) 17–36 (median, 26) 3–25 (median, 22) 2–76 (median, 16) 
Sex (male, female) 3, 1 1, 3 18, 20 
Blood drawn pre-IVIG (days after onset) 3–5 (median, 4) 5–9 (median, 6) 2–9 (median, 5) 
Blood drawn post-IVIG (days after onset) 8–14 (median, 12) 8–18 (median, 15) 7–18 (median, 13) 
Cardiac involvement (positive patients) 
Neutrophil count/mm3 (pre, post) 9,946 ± 3,792, 3,445 ± 763 11,203 ± 4,779, 3,322 ± 2,736 9,761 ± 3,353, 4,623 ± 5,110 
p valuea 0.04 0.02 0.002 
Monocyte count/mm3 (pre, post) 862 ± 540, 574 ± 290 1,434 ± 849, 818 ± 759 856 ± 700, 621 ± 587 
p valuea 0.42 0.16 0.06 
Lymphocyte count/mm3 (pre, post) 4,580 ± 735, 5,222 ± 3,055 5,380 ± 2,127, 5,988 ± 2,556 3,469 ± 1,885, 4,965 ± 2,361 
p valuea 0.72 0.75 0.001 
C reactive protein (mg/dl; pre, post) 10.4 ± 4.2, 0.8 ± 0.2 7.5 ± 3.4, 1.0 ± 0.6 10.1 ± 5.1, 1.9 ± 3.0 
p valuea 0.02 0.03 <0.001 
a

By paired t test, pre-IVIG vs post-IVIG.

Table II.

Background characteristics of patients in microarray study

PBMC 1PBMC 2PBMC 3PBMC 4Monocyte 1Monocyte 2Monocyte 3Monocyte 4
Age (mo after birth) 36 20 32 17 24 19 25 
Sex (male, female) 
IVIG (mg/kg × times) 400 × 4 400 × 4 400 × 5 400 × 5 400 × 5 and 670 × 3 1000 × 2 1000 × 2 1000 × 2 
Aspirin (mg/kg) 30 30 30 30 30 30 
Other medication None None Heparin (10 U/kg/h) during IVIG None None Heparin (10 U/kg/h) during IVIG None None 
Response to IVIG Well Well Well Well Well after second IVIG Well Well Well 
PBMC 1PBMC 2PBMC 3PBMC 4Monocyte 1Monocyte 2Monocyte 3Monocyte 4
Age (mo after birth) 36 20 32 17 24 19 25 
Sex (male, female) 
IVIG (mg/kg × times) 400 × 4 400 × 4 400 × 5 400 × 5 400 × 5 and 670 × 3 1000 × 2 1000 × 2 1000 × 2 
Aspirin (mg/kg) 30 30 30 30 30 30 
Other medication None None Heparin (10 U/kg/h) during IVIG None None Heparin (10 U/kg/h) during IVIG None None 
Response to IVIG Well Well Well Well Well after second IVIG Well Well Well 
FIGURE 1.

A, A Venn diagram of up- or down-regulated genes in PBMC and/or purified monocytes. Significantly (>2.0× or <0.5×) changed gene expressions are listed from the microarray results of PBMC and monocytes and indicated as a Venn diagram. B, Clustering analysis of the down-regulated transcripts in both PBMC and monocytes. Eighteen commonly down-regulated genes in A were analyzed by GeneSpring software, and a dendrogram was made from four paired monocyte samples obtained before and after IVIG therapy. Gene symbol, mean AD of four samples, and fold decrease in AD before vs after IVIG are presented in the right columns for each gene.

FIGURE 1.

A, A Venn diagram of up- or down-regulated genes in PBMC and/or purified monocytes. Significantly (>2.0× or <0.5×) changed gene expressions are listed from the microarray results of PBMC and monocytes and indicated as a Venn diagram. B, Clustering analysis of the down-regulated transcripts in both PBMC and monocytes. Eighteen commonly down-regulated genes in A were analyzed by GeneSpring software, and a dendrogram was made from four paired monocyte samples obtained before and after IVIG therapy. Gene symbol, mean AD of four samples, and fold decrease in AD before vs after IVIG are presented in the right columns for each gene.

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Because of the limited sample volumes obtained from patients and the necessity of avoiding activation of monocytes during the purification procedure, we used a RosetteSep monocyte enrichment system to negatively select monocytes. After enrichment, we determined the percentages of T, B, and NK cells by flow cytometry. In the monocyte-enriched fraction, each cell population was reduced to <1.0% of the total cell yield.

After analyzing gene expression profiles of monocyte-enriched fractions obtained from four additional KD patients (Tables I and II) using the Human Genome U133A probe array, 1274 transcripts were found to be differentially expressed after IVIG therapy (p = 0.05). Of these transcripts, 67 genes showed more than a doubling of expression compared with pre-IVIG levels, and 131 genes showed a decrease in expression to less than half the pre-IVIG levels after IVIG treatment (Fig. 1,A and supplemental table I4). A total of 18 genes showed consistently decreased transcripts after IVIG therapy in both PBMC and purified monocytes, suggesting that the decreases in these gene transcripts are not attributable to a reduced number of monocytes in PBMC after IVIG. A dendrogram of the expression profiles of these 18 genes is presented in Fig. 1 B together with their mean AD values calculated from four pairs of monocyte array results.

To confirm the GeneChip results and prove that these down-regulated genes were expressed mainly by monocytes in acute KD patients, we performed a real-time RT-PCR using negatively selected blood monocytes and T cells obtained from patients. We examined transcripts of six genes, FCGR1A, FCGR3A, CCR2, ADM, S100A9, and S100A12, as representatives of 18 genes commonly decreased in both PBMC and monocytes. The protein products of these genes were expected to have definite functions in the inflammatory process in patients, and their expression profiles in monocytes from the four patients differed slightly, as illustrated in Fig. 1,B. In addition, the transcripts of IL-6, IL-8, IL-10, and TNF-α were examined, because the array results did not indicate significant differences in their expression levels before vs after IVIG, although many reports have indicated that these inflammatory cytokines were overproduced in sera of acute KD patients (23, 24, 25, 26, 27). The S100A8 transcript was also examined, because this protein forms a heterocomplex with S100A9, which plays a role in the adhesion and chemotaxis of neutrophils and monocytes in the inflammatory response (28). Fig. 2 summarizes the real-time RT-PCR results of purified monocytes and T cells obtained from patients before and after IVIG. The transcripts of six genes that were down-regulated in GeneChip analysis, in addition to S100A8 and IL-10, were significantly decreased after IVIG therapy. The transcripts of IL-6, IL-8, and TNF-α were not significantly changed by IVIG. More importantly, the real-time RT-PCR confirmed that all six transcripts examined were expressed mainly in monocytes, and the contribution of T cells was much smaller than that of monocytes. Only TNF-α and IL-10 were expressed at compatible levels in monocytes and T cells.

FIGURE 2.

Higher expressions of FCGR1A, FCGR3A, CCR2, ADM, S100A9, and S100A12 genes in pre-IVIG compared with post-IVIG monocytes. Monocytes and T cells were negatively selected individually from PBMC, and real-time RT-PCR of each transcript was performed. Results are presented as relative units of each transcript compared with GAPDH. ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001 (compared with post-IVIG monocytes).

FIGURE 2.

Higher expressions of FCGR1A, FCGR3A, CCR2, ADM, S100A9, and S100A12 genes in pre-IVIG compared with post-IVIG monocytes. Monocytes and T cells were negatively selected individually from PBMC, and real-time RT-PCR of each transcript was performed. Results are presented as relative units of each transcript compared with GAPDH. ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001 (compared with post-IVIG monocytes).

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One proposed mechanism of the effect of IVIG therapy is a blockade of FcγRs on phagocytes. In this scenario, the bound IgG prevents immune complexes from being phagocytosed and from delivering an activating signal to the target cells. In a mouse model of ITP, another mechanism has been proposed that involves induction of inhibitory FcγRIIb by IVIG (29). However, the effect of IVIG on FcγR expression levels in KD has not been fully elucidated. Because both GeneChip results and the real-time PCR data suggested down-regulation of the activating FCGR1A and FCGR3A, we examined the surface expressions of these receptors on CD14+ monocytes from KD patients before and after IVIG treatment.

As shown in Fig. 3, FcγRI expression in KD patients (n = 12) was elevated before IVIG therapy compared with that in febrile controls (mean fluorescent intensity (MFI), 25.8 ± 3.2 vs 13.9 ± 1.2; p = 0.005) and decreased after IVIG treatment (MFI, 25.8 ± 3.2 vs 17.5 ± 1.6; p = 0.04). FcγRIII expression (n = 6) was also down-regulated by IVIG treatment (% positive, 20.5 ± 3.5 vs 10.7 ± 1.9%; p = 0.04), but there was no significant difference in FcγRIII expression levels between pre-IVIG KD patients and controls (% positive, 20.5 ± 3.5 vs 19.0 ± 4.4%; p = 0.79). FcγRII expression on monocytes (n = 12) was not significantly changed before vs after IVIG therapy (MFI, 42.3 ± 3.7 vs 38.4 ± 3.7; p = 0.50) and was not significantly elevated in KD patients compared with febrile controls (MFI, 42.3 ± 3.7 vs 33.2 ± 3.5; p = 0.12).

FIGURE 3.

Expressions of FcγRs on CD14+ monocytes from KD patients and febrile controls. Paired blood samples obtained before and after IVIG therapy from KD patients were double-stained with mAbs against CD14 and CD64 (n = 12), CD32 (n = 12), or CD16 (n = 6), respectively, and analyzed by flow cytometry. The results were expressed as MFI for CD64 and CD32 or as the percentage of cells positive for CD16 among CD14+ monocytes. The bar indicates the mean ± SEM in each group. ∗, p < 0.05 (compared with post-IVIG patients); ∗∗, p < 0.01 (compared with febrile controls).

FIGURE 3.

Expressions of FcγRs on CD14+ monocytes from KD patients and febrile controls. Paired blood samples obtained before and after IVIG therapy from KD patients were double-stained with mAbs against CD14 and CD64 (n = 12), CD32 (n = 12), or CD16 (n = 6), respectively, and analyzed by flow cytometry. The results were expressed as MFI for CD64 and CD32 or as the percentage of cells positive for CD16 among CD14+ monocytes. The bar indicates the mean ± SEM in each group. ∗, p < 0.05 (compared with post-IVIG patients); ∗∗, p < 0.01 (compared with febrile controls).

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Among the 18 genes significantly decreased after IVIG therapy in both PBMC and monocytes, a member of the S100 protein family, S100A9, was of particular interest, because this protein as well as its partner protein, S100A8, are predominantly expressed in neutrophils and monocytes and are excreted into the circulation under inflammatory conditions. In plasma, the S100A8/A9 heterocomplex is the main form of the two proteins and has been shown to enhance monocyte adhesion to endothelial cells and to cause neutrophil chemotaxis. Therefore, we measured plasma levels of the S100A8/A9 heterocomplex as well as the S100A9 homocomplex in KD patients before and after IVIG therapy.

As shown in Fig. 4, plasma S100A8/A9 heterocomplex levels were significantly higher in pre-IVIG (n = 32) than in post-IVIG patients and febrile controls (25.3 ± 1.5 vs 18.4 ± 1.7 mg/ml (p = 0.001) vs 10.7 ± 1.0 μg/ml (p < 0.0001), respectively). In contrast, plasma S100A9 homocomplex levels were significantly lower in pre-IVIG (n = 31) than in post-IVIG patients (12.8 ± 2.6 vs 20.6 ± 2.8 ng/ml; p < 0.0001), but were not significantly different from those in febrile controls (vs 12.5 ± 5.8 ng/ml). Moreover, the decrease in the S100A8/A9 heterocomplex after IVIG was absent in four of six patients who had cardiac involvement during the acute phase of the disease (p = 0.02, by χ2 test with Yates’ correction). Correlation analysis showed S100A8/A9 levels in post-IVIG patients to correlate significantly with the monocyte counts, neutrophil counts, and CRP levels of post-IVIG patients (Table IV).

FIGURE 4.

Elevated plasma S100A8/A9 levels in pre-IVIG patients. Concentrations of the S100A8/A9 heterocomplex (n = 32) and the S100A9 homocomplex (n = 30) in heparinized plasma were measured by ELISA in pre-IVIG and post-IVIG KD patients. •, Patients with cardiac involvement; ○, patients without cardiac involvement. The bar indicates the mean ±] SEM for each group. ∗, p = 0.001 (compared with post-IVIG values); ∗∗, p < 0.0001 (compared with post-IVIG or febrile controls).

FIGURE 4.

Elevated plasma S100A8/A9 levels in pre-IVIG patients. Concentrations of the S100A8/A9 heterocomplex (n = 32) and the S100A9 homocomplex (n = 30) in heparinized plasma were measured by ELISA in pre-IVIG and post-IVIG KD patients. •, Patients with cardiac involvement; ○, patients without cardiac involvement. The bar indicates the mean ±] SEM for each group. ∗, p = 0.001 (compared with post-IVIG values); ∗∗, p < 0.0001 (compared with post-IVIG or febrile controls).

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Table IV.

Correlation coefficient between S100 proteins plasma levels and laboratory dataa

S100A8/A9 PreS100A8/A9 PostS100A9 PreS100A9 Post
Neutrophil pre 0.25 0.24 −0.23 −0.11 
Neutrophil post −0.10 0.61b −0.02 −0.08 
     
Monocyte pre 0.03 0.02 −0.13 0.26 
Monocyte post −0.27 0.52c 0.06 0.09 
     
CRP pre 0.23 0.11 −0.13 −0.24 
CRP post −0.15 0.76d 0.15 −0.13 
S100A8/A9 PreS100A8/A9 PostS100A9 PreS100A9 Post
Neutrophil pre 0.25 0.24 −0.23 −0.11 
Neutrophil post −0.10 0.61b −0.02 −0.08 
     
Monocyte pre 0.03 0.02 −0.13 0.26 
Monocyte post −0.27 0.52c 0.06 0.09 
     
CRP pre 0.23 0.11 −0.13 −0.24 
CRP post −0.15 0.76d 0.15 −0.13 
a

Significance was determined by Spearman’s correlation coefficient by rank.

b

p < 0.001.

c

p < 0.01.

d

p < 0.0001.

The aim of this study was to clarify the molecular events following administration of high dose Ig to KD patients. We were especially interested in which cell population in the peripheral circulation was more affected by IVIG therapy with regard to the transcript profile. For this purpose, we first examined gene expression in PBMC obtained from KD patients before IVIG therapy and just after the therapy, when acute symptoms had resolved in most of the patients. The results suggested that IVIG acts in a broad functional range in PBMC, favoring down-regulation. Among the 89 genes whose expressions were more than doubled or reduced to no more than half the pre-IVIG level, 96% (85 genes) were down-regulated. This is consistent with the suppressive effects of IVIG observed clinically in acute patients whose body temperatures as well as other laboratory markers of inflammation dropped rapidly in response to therapy. More interestingly, by functional classification, as many as 17 down-regulated genes (19.1%) were cell surface receptor molecules, 16 of which were expressed mainly in monocytes and macrophages. The predominance of monocyte-derived transcripts was also recognized in other functional categories, such as secreted peptides (ADM, S100A8, S100A9, S100A12, endothelial cell growth factor 1, and TNF super family 13) and metabolic enzymes (hexokinase 3, cytochrome P450 family 1, subfamily B, polypeptide 1, histidine ammonia-lyase, alanyl aminopeptidase, and neutrophil cytosolic factor 2). Although we did not examine neutrophil mRNA in this study, our findings clearly indicate that IVIG acts, at least in part, by suppressing activated monocytes and macrophages in KD patients.

The oligonucleotide array analysis of the purified monocyte fraction from KD patients confirmed the results obtained by PBMC. By analyzing gene expression profiles of purified monocytes, the number of transcripts differentially expressed pre-IVIG vs post-IVIG was doubled compared with the expression in PBMC. In this assay IVIG not only down-regulated the expression of 131 genes, but also up-regulated 67 genes in patients (Fig. 1 A and Supplementary Table I). The increased number of down-regulated genes in addition to the newly identified up-regulated genes might indicate increased sensitivity of the microarray assay, attributable to using a more homogeneous cell population than PBMC. Among the down-regulated transcripts newly identified by this method, the cell surface receptor molecules again formed the largest functional family. These include receptors important in the initiation of innate immune responses (TLR1 and TLR4), receptors functioning in phagocytosis and Ag presentation (asialoglycoprotein receptor 2, complement 3b/4b receptor 1, and stabilin 1), and receptors involved in cell adhesion (platelet/endothelial cell adhesion molecule, selectin P ligand, and bone marrow stromal cell Ag 1). In addition to these surface receptors, the transcripts of some secreted proteins that work as pattern recognition molecules and enhance phagocytosis by macrophages (complement component 1q α and β, and ficolin 1) were newly recognized as being decreased after IVIG therapy. These results suggest that IVIG exerts a broad range of effects, suppressing monocyte and macrophage functions. Although down-regulation of inflammatory cytokines such as TNF-α and IL-6 after IVIG therapy has been reported by others (14, 15, 16), we found no significant differences in these cytokine transcripts between pre- and post-IVIG patients in this study. The translation of many inflammatory cytokines has been demonstrated to be regulated by factors that control mRNA stability (30, 31). Therefore, IVIG may affect the monocyte production of these cytokines by destabilizing their mRNA. Alternatively, IVIG may affect the production of these cytokines differently in PBMC and other cells, such as vascular endothelial cells and hepatocytes. In this respect, it would be important to determine whether IVIG affects the production of these cytokines by other cell types and whether suppression of monocyte function by IVIG, as observed in this study, is due to reductions of these inflammatory signals outside PBMC.

Eighteen genes were down-regulated by IVIG in both PBMC and monocyte array experiments. Because only a limited number of patients were available for the microarray analysis, we performed real-time PCR to examine six of these 18 transcripts and confirmed the microarray results. Of these genes, plasma levels of ADM and S100A12 have been reported to be elevated in KD patients (32, 33, 34). Nomura et al. (32) demonstrated, using oligonucleotide microarray analysis, that ADM and S100A12 mRNA are highly expressed in acute KD patients, and Nishida et al. (33) showed plasma ADM levels to be higher in patients who subsequently developed coronary aneurysms than in patients who did not (33). Foell et al. (34) reported the plasma S100A12 level to be high in pre-IVIG patients and to decrease quickly after IVIG therapy. MCP-1, a ligand of CCR2, was also known to be highly expressed in acute KD patients, especially in cardiac tissue (14). Thus, the down-regulation of CCR2 in monocytes after IVIG therapy as well as ADM and S100A12 may be beneficial for patients by reducing the accumulation of monocytes at the inflammatory sites and preventing coronary aneurysms. Besides these genes, we focused on down-regulation of the activating FCGR genes and the S100A9 gene in response to IVIG therapy. The interaction of IVIG with FcγRs on monocytes/macrophages has been proposed as a mechanism underlying the therapeutic effect of IVIG in several autoimmune diseases, such as ITP and autoantibody-induced arthritis in mice (29, 35). In these diseases, IVIG induces low affinity inhibitory FcγRIIb on a certain population of monocytes and counteracts the consumption of platelets or the inflammatory response, respectively, elicited by the interaction between immune complexes and the low affinity activating FcγRIIIa. In our study the transcripts of activating FCGR1A, FCGR2A, and FCGR3A genes were reduced, and the expressions of FcγRI and FcγRIII on CD14+ monocytes were down-regulated after IVIG in KD patients. Although we could not determine whether the expression of activating FcγRIIa and the inhibitory FcγRIIb changed independently, the staining intensity for CD32 was unchanged after IVIG in our patients, and FCGR2B transcripts were not increased after IVIG in the array experiment. Nevertheless, IVIG may act by modulating the ratio of activation to inhibitory FcγR expression on monocytes so as to raise the threshold for monocyte excitation in KD. Reports have been accumulating that suggest dysregulation of such a balance between activation and inhibitory FcγR expression to possibly contribute to autoimmune disease pathogenesis (36, 37, 38, 39). In addition, several autoantibodies have been implicated in the acute phase of KD (39, 40). The association of the down-regulation of these activating FcγRs with the therapeutic effect of IVIG in KD might suggest a new venue for research into the pathogenesis of this disease.

S100 calcium-binding proteins A8 and A9 are expressed by peripheral blood monocytes and neutrophils, and the production of these proteins is up-regulated by LPS, IFN-γ, IL-1, and TNF-α (41, 42, 43). Increased serum levels have been demonstrated in many inflammatory diseases, such as rheumatoid arthritis, cystic fibrosis, Crohn’s disease, and infection (44, 45, 46, 47). The two proteins form noncovalent homodimers and a heterocomplex (S100A8/A9) in a calcium-dependent manner (48). Recently, all these monomers and dimers have been recognized to induce neutrophil and monocyte chemotaxis and adhesion in a mouse air pouch model of inflammation (28). S100A9 and S100A8/A9 also enhance monocyte adhesion to vascular endothelial cells (49, 50). In our study, plasma S100A8/A9, but not S100A9, levels were elevated in acute KD patients compared with febrile controls and were rapidly down-regulated after IVIG therapy. In post-IVIG patients, S100A8/A9 levels correlated closely with the plasma CRP levels and neutrophil and monocyte counts. Persistent elevation of plasma S100A8/A9 levels after IVIG was related to a higher risk of developing coronary aneurysms in KD patients. These findings suggest that the failure of IVIG to suppress S100A8/A9 expression in monocytes and other cell populations might result in the continued recruitment and stimulation of neutrophils and monocytes at the inflammatory sites. Alternatively, the persistent production of S100A8/A9 might be a secondary phenomenon caused by prolonged survival of activated monocytes and neutrophils. We favor the former explanation, because our findings add to the accumulating evidence of direct effects of S100A8/A9 on both monocytes and endothelial cells (50, 51, 52, 53). Eue et al. (50, 53) reported that S100A8/A9 was secreted by activated monocytes and that protein production was enhanced by the cell-cell interaction of monocytes with activated endothelial cells. They also reported that S100A8/A9 and S100A9 bound to resting monocytes and TNF-α-activated microvascular endothelial cells in a dose-dependent and saturable manner and enhanced CD11b expression on monocytes. It is important to clarify the role of the S100A8/A9 heterocomplex in KD pathogenesis, especially in relation to the interaction between activated monocytes/neutrophils and vascular endothelial cells.

In conclusion, the results of this study suggest that IVIG therapy in KD patients suppresses activated peripheral blood monocytes and macrophages by down-regulating various functional genes. Among these genes, we focused on two pathways that IVIG may use to suppress inflammation in KD patients. One is homeostatic control of the expression of activating vs inhibitory FcγRs on monocytes. The other is suppression of S100A8/A9 heterocomplex production in patients. It is important to further elucidate the precise molecular mechanisms of IVIG in these two pathways to monitor the effectiveness of IVIG in patients and to develop a new molecular target for treating KD patients.

We are particularly grateful to the pediatricians at Hachiouji Metropolitan Children’s Hospital and Chiba University Hospital for providing blood samples. We thank Hiromi Wakita, Naomi Wada, and Noriko Hashimoto for their excellent technical assistance.

The authors have no financial conflict of interest.

Table III.

Up- and down-regulated genes in PBMC

Gene TitleGene SymbolMean ADFold Decrease
BeforeAfter
Cell surface molecules and receptors     
 IgG FcRIa (CD64) FCGR1A 320.9 80.7 3.98 
 Leukocyte Ig-like receptor B1 LILRB1 256.0 72.3 3.54 
 Leukocyte Ig-like receptor B2 LILRB2 644.6 195.8 3.29 
 IgG FcRIIa (CD32) FCGR2A 394.2 123.4 3.19 
 TLR2 TLR2 479.9 154.8 3.10 
 Adiponectin receptor 1 ADIPOR1 1356.5 450.7 3.01 
 Formyl peptide receptor 1 FPR1 1568.9 542.8 2.89 
 CSF 2 receptor b CSF2RB 646.8 225.1 2.87 
 IL-8Rb IL8RB 108.6 38.8 2.80 
 Stabilin 1 STAB1 792.1 298.0 2.66 
 Leukocyte Ig-like receptor B3 LILRB3 342.0 129.4 2.64 
 Ectonucleoside triphosphate diphosphohydrolase 1 (CD39) ENTPD1 209.5 86.2 2.43 
 Chemokine (CC motif) receptor 2 CCR2 467.3 194.2 2.41 
 IgG FcRIIIa (CD16) FCGR3A 500.6 208.7 2.40 
 CSF 1 receptor CSF1R 511.0 214.5 2.38 
 Protein tyrosine phosphatase, non-receptor type substrate 1 PTPNS1 280.8 119.1 2.36 
 Leukocyte-specific transcript 1 LST1 512.6 223.4 2.29 
Apoptosis and cell growth     
 Cold autoinflammatory syndrome 1 CIAS1 246.9 42.4 5.83 
 Cyclin-dependent kinase inhibitor 1C (p57, Kip2) CDKN1C 323.1 110.5 2.92 
 S-phase response (cyclin-related) SPHAR 162.5 59.1 2.75 
 IL-3-regulated NF NFIL3 441.2 168.7 2.61 
 Growth arrest-specific 7 GAS7 755.4 337.2 2.24 
Secreted molecules     
 ADM ADM 255.3 54.9 4.65 
 S100 calcium/binding protein A9 S100A9 5820.1 1331.7 4.37 
 S100 calcium-binding protein A12 S100A12 1423.8 341.9 4.16 
 Chondroitin sulfate proteoglycan 2 (versican) CSPG2 5358.5 1714.7 3.12 
 S100 calcium-binding protein A8 S100A8 13143.9 4509.0 2.92 
 Pre-B-cell colony-enhancing factor PBEF 1593.3 558.8 2.85 
 Proapoptotic caspase adaptor protein PACAP 618.0 254.6 2.43 
 Ig λ L chain IGL 479.0 197.8 2.42 
 Endothelial cell growth factor 1 ECGF1 439.4 186.5 2.36 
 TNF ligand 13 (APRIL) TNFSF13 217.7 105.6 2.06 
Signal transduction     
 Dysferlin DYSF 544.8 150.8 3.61 
 Chimerin 2 CHN2 220.3 61.5 3.58 
 Hemopoietic cell kinase HCK 1084.1 322.8 3.36 
 Dual specificity phosphatase 1 DUSP1 5960.5 2042.8 2.92 
 Regulator of G-protein signalling 2 RGS2 4329.8 1565.5 2.77 
 RAB31 RAB31 605.0 229.3 2.64 
 Ribosome-binding protein 1 RRBP1 327.2 143.4 2.28 
Transcription factor     
 v-fos homolog FOS 1964.8 315.4 6.23 
 v-fos homolog B FOSB 643.9 111.4 5.78 
 Kruppel-like factor 4 KLF4 763.1 160.9 4.74 
 Cold shock domain protein A CSDA 1529.6 397.9 3.84 
 Early growth response 1 EGR1 1410.5 373.6 3.78 
 v-ets homolog 2 ETS2 243.7 73.2 3.33 
 SFFV proviral integration 1 SPI1 330.8 102.3 3.23 
 Calreticulin CALR 277.4 105.5 2.63 
 MHC class II transactivator MHC2TA 277.6 108.1 2.57 
 Transcription factor 7-like 2 TCF7L2 308.9 131.5 2.35 
 B cell CLL/lymphoma 6 BCL6 1242.2 507.2 2.45 
Metabolism     
 Hexokinase 3 HK3 544.5 43.3 12.57 
 Aminolevulinate synthase 2 ALAS2 2947.7 445.8 6.61 
 Chromosome 20 open reading frame 16 C20orf16 491.7 121.2 4.06 
 Guanosine monophosphate reductase GMPR 243.2 63.2 3.85 
 Cytochrome P450 family 1B polypeptide 1 CYP1B1 606.7 178.0 3.41 
 Fatty acid-coenzyme A ligase long-chain 2 FACL2 696.6 217.2 3.21 
 Biliverdin reductase B (NADPH) BLVRB 418.2 131.7 3.18 
 Histidine ammonia-lyase HAL 192.4 62.0 3.11 
 IFN-γ-inducible protein 30 IFI30 2168.7 773.0 2.81 
 Alanyl aminopeptidase (CD13) ANPEP 541.8 195.0 2.78 
 Flavoprotein oxidoreductase MICAL2 659.7 238.5 2.77 
 Neutrophil cytosolic factor 2 NCF2 941.7 351.6 2.68 
 Spermidine/spermine N1-acetyltransferase SAT 1215.9 480.5 2.53 
 Cathepsin Z CTSZ 146.6 58.0 2.53 
Gene TitleGene SymbolMean ADFold Decrease
BeforeAfter
Cell surface molecules and receptors     
 IgG FcRIa (CD64) FCGR1A 320.9 80.7 3.98 
 Leukocyte Ig-like receptor B1 LILRB1 256.0 72.3 3.54 
 Leukocyte Ig-like receptor B2 LILRB2 644.6 195.8 3.29 
 IgG FcRIIa (CD32) FCGR2A 394.2 123.4 3.19 
 TLR2 TLR2 479.9 154.8 3.10 
 Adiponectin receptor 1 ADIPOR1 1356.5 450.7 3.01 
 Formyl peptide receptor 1 FPR1 1568.9 542.8 2.89 
 CSF 2 receptor b CSF2RB 646.8 225.1 2.87 
 IL-8Rb IL8RB 108.6 38.8 2.80 
 Stabilin 1 STAB1 792.1 298.0 2.66 
 Leukocyte Ig-like receptor B3 LILRB3 342.0 129.4 2.64 
 Ectonucleoside triphosphate diphosphohydrolase 1 (CD39) ENTPD1 209.5 86.2 2.43 
 Chemokine (CC motif) receptor 2 CCR2 467.3 194.2 2.41 
 IgG FcRIIIa (CD16) FCGR3A 500.6 208.7 2.40 
 CSF 1 receptor CSF1R 511.0 214.5 2.38 
 Protein tyrosine phosphatase, non-receptor type substrate 1 PTPNS1 280.8 119.1 2.36 
 Leukocyte-specific transcript 1 LST1 512.6 223.4 2.29 
Apoptosis and cell growth     
 Cold autoinflammatory syndrome 1 CIAS1 246.9 42.4 5.83 
 Cyclin-dependent kinase inhibitor 1C (p57, Kip2) CDKN1C 323.1 110.5 2.92 
 S-phase response (cyclin-related) SPHAR 162.5 59.1 2.75 
 IL-3-regulated NF NFIL3 441.2 168.7 2.61 
 Growth arrest-specific 7 GAS7 755.4 337.2 2.24 
Secreted molecules     
 ADM ADM 255.3 54.9 4.65 
 S100 calcium/binding protein A9 S100A9 5820.1 1331.7 4.37 
 S100 calcium-binding protein A12 S100A12 1423.8 341.9 4.16 
 Chondroitin sulfate proteoglycan 2 (versican) CSPG2 5358.5 1714.7 3.12 
 S100 calcium-binding protein A8 S100A8 13143.9 4509.0 2.92 
 Pre-B-cell colony-enhancing factor PBEF 1593.3 558.8 2.85 
 Proapoptotic caspase adaptor protein PACAP 618.0 254.6 2.43 
 Ig λ L chain IGL 479.0 197.8 2.42 
 Endothelial cell growth factor 1 ECGF1 439.4 186.5 2.36 
 TNF ligand 13 (APRIL) TNFSF13 217.7 105.6 2.06 
Signal transduction     
 Dysferlin DYSF 544.8 150.8 3.61 
 Chimerin 2 CHN2 220.3 61.5 3.58 
 Hemopoietic cell kinase HCK 1084.1 322.8 3.36 
 Dual specificity phosphatase 1 DUSP1 5960.5 2042.8 2.92 
 Regulator of G-protein signalling 2 RGS2 4329.8 1565.5 2.77 
 RAB31 RAB31 605.0 229.3 2.64 
 Ribosome-binding protein 1 RRBP1 327.2 143.4 2.28 
Transcription factor     
 v-fos homolog FOS 1964.8 315.4 6.23 
 v-fos homolog B FOSB 643.9 111.4 5.78 
 Kruppel-like factor 4 KLF4 763.1 160.9 4.74 
 Cold shock domain protein A CSDA 1529.6 397.9 3.84 
 Early growth response 1 EGR1 1410.5 373.6 3.78 
 v-ets homolog 2 ETS2 243.7 73.2 3.33 
 SFFV proviral integration 1 SPI1 330.8 102.3 3.23 
 Calreticulin CALR 277.4 105.5 2.63 
 MHC class II transactivator MHC2TA 277.6 108.1 2.57 
 Transcription factor 7-like 2 TCF7L2 308.9 131.5 2.35 
 B cell CLL/lymphoma 6 BCL6 1242.2 507.2 2.45 
Metabolism     
 Hexokinase 3 HK3 544.5 43.3 12.57 
 Aminolevulinate synthase 2 ALAS2 2947.7 445.8 6.61 
 Chromosome 20 open reading frame 16 C20orf16 491.7 121.2 4.06 
 Guanosine monophosphate reductase GMPR 243.2 63.2 3.85 
 Cytochrome P450 family 1B polypeptide 1 CYP1B1 606.7 178.0 3.41 
 Fatty acid-coenzyme A ligase long-chain 2 FACL2 696.6 217.2 3.21 
 Biliverdin reductase B (NADPH) BLVRB 418.2 131.7 3.18 
 Histidine ammonia-lyase HAL 192.4 62.0 3.11 
 IFN-γ-inducible protein 30 IFI30 2168.7 773.0 2.81 
 Alanyl aminopeptidase (CD13) ANPEP 541.8 195.0 2.78 
 Flavoprotein oxidoreductase MICAL2 659.7 238.5 2.77 
 Neutrophil cytosolic factor 2 NCF2 941.7 351.6 2.68 
 Spermidine/spermine N1-acetyltransferase SAT 1215.9 480.5 2.53 
 Cathepsin Z CTSZ 146.6 58.0 2.53 
Table IIIA.

(Continued)

Gene TitleGene SymbolMean ADFold Decrease
BeforeAfter
 Exostoses 1 EXT1 109.6 49.0 2.24 
 Dihydropyrimidinase-like 2 DPYSL2 738.1 338.3 2.18 
 Ribosomal protein S11 RPS11 160.8 324.1 −2.02 
Transport     
 Aquaporin 9 AQP9 521.3 137.9 3.78 
 Mitochondrial solute carrier protein MSCP 558.9 190.4 2.94 
 Solute carrier family 11 member 1 SLC11A1 560.7 194.7 2.88 
 Phospholipid scramblase 1 PLSCR1 202.9 71.3 2.85 
 Protein disulfide isomerase-related protein P5 1123.4 444.8 2.53 
 Heat shock 70 kDa protein 6 (HSP70B′) HSPA6 592.7 238.0 2.49 
 Hypothetical protein BC013764 LOC115207 583.9 251.3 2.32 
 NK-tumor recognition sequence NKTR 40.6 93.0 −2.29 
Gene TitleGene SymbolMean ADFold Decrease
BeforeAfter
 Exostoses 1 EXT1 109.6 49.0 2.24 
 Dihydropyrimidinase-like 2 DPYSL2 738.1 338.3 2.18 
 Ribosomal protein S11 RPS11 160.8 324.1 −2.02 
Transport     
 Aquaporin 9 AQP9 521.3 137.9 3.78 
 Mitochondrial solute carrier protein MSCP 558.9 190.4 2.94 
 Solute carrier family 11 member 1 SLC11A1 560.7 194.7 2.88 
 Phospholipid scramblase 1 PLSCR1 202.9 71.3 2.85 
 Protein disulfide isomerase-related protein P5 1123.4 444.8 2.53 
 Heat shock 70 kDa protein 6 (HSP70B′) HSPA6 592.7 238.0 2.49 
 Hypothetical protein BC013764 LOC115207 583.9 251.3 2.32 
 NK-tumor recognition sequence NKTR 40.6 93.0 −2.29 

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

This work was supported in part by a Grant for Child Health and Development (14-2) from the Ministry of Health, Labor, and Welfare; a grant from the Japan Health Sciences Foundation; and a grant from the Organization for Pharmaceutical Safety and Research of the Ministry of Health, Labor, and Welfare (Millenium Genome Project, MPJ-5).

3

Abbreviations used in this paper: KD, Kawasaki disease; AD, average difference; ADM, adrenomedullin; CRP, C-reactive peptide; ITP, idiopathic thrombocytopenic purpura; IVIG, i.v. infusion of high dose Ig; MFI, mean fluorescent intensity; S100, S100 calcium-binding protein family.

4

The online version of this article contains supplemental material.

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