Gene expression profiling in monocytes and SNP association suggest the importance of the STAT1 gene for osteoporosis in both Chinese and Caucasians

Authors

  • Xiang-Ding Chen,

    1. Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, Changsha, Hunan, Peoples Republic of China
    2. Osteoporosis Research Center and Department of Biomedical Sciences, Creighton University Medical Center, Omaha, NE, USA
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  • Peng Xiao,

    1. Osteoporosis Research Center and Department of Biomedical Sciences, Creighton University Medical Center, Omaha, NE, USA
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  • Shu-Feng Lei,

    1. Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, Changsha, Hunan, Peoples Republic of China
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  • Yao-Zhong Liu,

    1. Departments of Orthopedic Surgery and Basic Medical Sciences, University of Missouri–Kansas City, Kansas City, MO, USA
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  • Yan-Fang Guo,

    1. Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, Changsha, Hunan, Peoples Republic of China
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  • Fei-Yan Deng,

    1. Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, Changsha, Hunan, Peoples Republic of China
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  • Li-Jun Tan,

    1. Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, Changsha, Hunan, Peoples Republic of China
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  • Xue-Zhen Zhu,

    1. Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, Changsha, Hunan, Peoples Republic of China
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  • Fu-Rong Chen,

    1. Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, Changsha, Hunan, Peoples Republic of China
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  • Robert R. Recker,

    1. Osteoporosis Research Center and Department of Biomedical Sciences, Creighton University Medical Center, Omaha, NE, USA
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  • Hong-Wen Deng

    Corresponding author
    1. Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, Changsha, Hunan, Peoples Republic of China
    2. Departments of Orthopedic Surgery and Basic Medical Sciences, University of Missouri–Kansas City, Kansas City, MO, USA
    • College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, Peoples Republic of China.
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Abstract

Osteoporosis is characterized mainly by low bone mineral density (BMD). Many cytokines and chemokines have been related with bone metabolism. Monocytes in the immune system are important sources of cytokines and chemokines for bone metabolism. However, no study has investigated in vivo expression of a large number of various factors simultaneously in human monocytes underlying osteoporosis. This study explored the in vivo expression pattern of general cytokines, chemokines, and their receptor genes in human monocytes and validated the significant genes by qRT-PCR and genetic association analyses. Expression profilings were performed in monocyte samples from 26 Chinese and 20 Caucasian premenopausal women with discordant BMD. Genome-wide association analysis with BMD variation was conducted in 1000 unrelated Caucasians. We selected 168 cytokines, chemokines, osteoclast-related factors, and their receptor genes for analyses. Significantly, the signal transducer and activator of transcription 1 (STAT1) gene was upregulated in the low versus the high BMD groups in both Chinese and Caucasians. We also revealed a significant association of the STAT1 gene with BMD variation in the 1000 Caucasians. Thus we conclude that the STAT1 gene is important in human circulating monocytes in the etiology of osteoporosis. © 2010 American Society for Bone and Mineral Research

Introduction

Osteoporosis is mainly characterized by low bone mineral density (BMD). Genetic factors have important influences on BMD and osteoporosis.1–3 Recent studies have shown that the immune system is strongly related to bone metabolism in terms of osteoimmunology.4–8 Pathologic bone resorption was observed in immune system–related diseases such as autoimmune arthritis, periodontitis, Paget's disease, and bone tumors.9

Monocytes, important cells in immune system, produce a wide variety of factors such as interleukin 1 (IL-1), IL-6, tumor necrosis factor (TNF), transforming growth fator beta (TGF-β), and 1,25-dihydroxyvitamin D3 [1,25(OH)2D3].10 These factors are involved in bone metabolism by regulating osteoclastic differentiation. Monocytes are also potential precursors of osteoclasts.11, 12 In vitro studies demonstrated that monocytes can differentiate into osteoclasts with bone resorption function.13, 14

However, it is unknown whether other factors and mechanisms to regulate these factors are important in the ability of monocytes to affect bone metabolism. To address these questions, scientists have screened the differential gene expressions in osteoclastogenic cells using a high-throughput microarray platform.15, 16 Microarray technology has been used successfully for detection of gene expression profiles in diseases such as inflammatory breast cancer and urinary bladder cancer.17, 18 Theoretical studies also supported the reliability of using a microarray platform for the quantitative characterization of gene expression.19, 20 However, differential gene expression profiles in circulating monocytes associated with BMD variation had not been investigated until our previous research in Caucasian females.16 In that study, we showed that chemokine receptor 3 (CCR3), histidine decarboxylase (HDC), and glucocorticoid receptor (GCR) genes in circulating monocytes potentially contributed to bone metabolism.16

The present study aims to identify significantly differentially expressed genes from 168 selected cytokine, chemokine, and osteoclastogenesis-related genes in circulating monocytes between the high and low BMD groups in Chinese Han females and validate the significant expression in Caucasian women. We also performed single-nucleotide polymorphism (SNP) association analysis with BMD to find further evidence of the identified genes at the DNA level.

Materials and Methods

Chinese subjects

The study was approved by the Research Administration Department of Hunan Normal University. Eight hundred and seventy-eight females who were Chinese Hans were recruited from Changsha City. All subjects signed informed-consent documents before entering the project. Healthy female subjects aged of 20 to 45 years were included because BMD reaches its peak and is most stable during this period. For each subject, we collected information on age, sex, medical history, family history, menstrual history, smoking history, physical activity, alcohol use, tea and coffee consumption, diet habits, etc. Female subjects must have regular menses to avoid the effects of menopause on BMD. Subjects with chronic diseases and conditions that potentially may affect bone mass have been excluded from the study. These diseases/conditions included chronic disorders involving vital organs (e.g., heart, lung, liver, kidney, and brain), serious metabolic diseases (e.g., diabetes, hypo- and hyperparathyroidism, and hyperthyroidism, etc.), skeletal diseases (e.g., Paget's disease, osteogenesis imperfecta, and rheumatoid arthritis, etc.), chronic use of drugs affecting bone metabolism (e.g., corticosteroid therapy and anticonvulsant drugs), and malnutrition conditions (e.g., chronic diarrhea, chronic ulcerative colitis, etc.). From the 100 top and 100 bottom hip BMD subjects we recruited all who consented to enter our potential future projects, including 14 high hip BMD (mean ± SD = 1.03 ± 0.05 g/cm2) subjects and 12 low hip BMD (mean ± SD = 0.7 ± 0.06 g/cm2) subjects (Table 1). Thirty milliliters of peripheral blood were drawn for each selected subject.

Table 1. Basic Characteristic Description of the Study Subjects in Chinese and Caucasians
TraitFemale Chinese for gene expressionFemale Caucasians for gene expressionMale Caucasians for SNP associationFemale Caucasians for SNP association
Low BMD(n = 12)High BMD (n = 14)Low BMD (n = 10)High BMD (n = 10)≤50 years (n = 250)>50 years (n = 251)Premenopausal (n = 249)Postmenopausal (n = 250)
  1. Note: Values are the mean ± SD.

Age years25.28 ± 3.1428.67 ± 4.7242.90 ± 1.9141.70 ± 1.8933.44 ± 9.6667.33 ± 6.7433.97 ± 8.4566.36 ± 5.67
Height (cm)158.88 ± 4.36158.93 ± 5.28160.46 ± 5.01166.96 ± 7.30180.00 ± 6.78175.67 ± 6.63165.38 ± 6.13162.22 ± 6.43
Weight (kg)51.54 ± 7.3155.84 ± 5.7358.00 ± 7.5391.64 ± 19.5888.03 ± 15.3590.04 ± 14.4770.74 ± 16.5171.71 ± 5.10
Spine BMD (g/cm2)0.85 ± 0.071.04 ± 0.090.90 ± 0.081.21 ± 0.081.05 ± 0.121.08 ± 0.201.05 ± 0.110.94 ± 0.10
Hip BMD (g/cm2)0.70 ± 0.061.03 ± 0.050.79 ± 0.081.14 ± 0.091.07 ± 0.151.01 ± 0.140.95 ± 0.120.86 ± 0.14

BMD measurement

BMD (g/cm2) at the lumbar spine (L1–4, anteroposterior view) and total hip (femoral neck, trochanter, and intertrochanter region) was measured by a Hologic 4500-W dual-energy X-ray absorptiometry (DXA) (Hologic Corp., Waltham, MA, USA). The DXA scanner was calibrated daily, and long-term precision was monitored with external spine and hip phantoms. The coefficient of variation (CV) of measured BMD values was 0.80% at the hip.

Monocyte isolation

A monocyte negative isolation kit (Dynal Biotech, Inc., Lake Success, NY, USA) was used to isolate circulating monocytes from 30 mL of whole blood following the procedures recommended by the manufacturer. The kit contains a mixture of antibodies for CD2, CD7, CD16, CD19, CD56, and CD235a to deplete T cells, B cells, natural killer cells, erythrocytes, and granulocytes (if present), leaving monocytes untouched, pure, viable, and free of the surface-bound antibody and beads. Monocyte purity was assessed by flow cytometry (BD Biosciences, San Jose, CA, USA) with fluorescence-labeled antibodies PE-CD14 and FITC-CD45. The purity was 86% on average (Fig. 1).

Figure 1.

Flow cytometer analysis of the percentage of CD14+/CD45+ cells from human blood. CD14 and CD45 are the specific membrane markers on monocytes and mononuclear cells, respectively.

Total RNA extraction and microarray procedure

Total RNA from monocytes was extracted using a Qiagen RNeasy Mini Kit (Qiagen, Inc., Valencia, CA, USA). RNA integrity was assessed by using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). A total of 10 µg RNA from each sample was converted into biotinylated fragmented cRNA (BioArray HighYield RNA Transcription Labeling Kit, Enzo Diagnostics) that was hybridized (Affymetrix Genechip Hybridization Oven 640) to HG-U133 plus 2.0 GeneChip oligonucleotide arrays (Affymetrix, Santa Clara, CA, USA), which contains 54,675 sets of oligonucleotide probes that correspond to approximately 38,500 unique human genes, and then washed (Affymetrix Fluidics Station 450), stained with phycoerythrin-streptavidin, and scanned using Affymetrix Gene Array Scanner 3000.

Statistical analysis

Transciptome-wide expression profiling involves a large number of genes and thus incorporates tremendous multiple tests. Some genes with suggestive significance may be excluded after the multiple-testing correction. If some genes are tightly related in a functionally relevant pathway, the P value for any single gene may not be significant after the multiple-testing correction. However, a focused expression screen on potential functional relevant genes largely can reduce the multiple tests and may increase the statistical power. Considering the multicomparison problem and the function of monocytes, 168 candidate genes were selected for statistical analyses. The 168 genes are all available cytokines, chemokines, osteoclast-related factors, and their receptors selected for our focused expression analyses of the Affymetrix HG133 plus 2.0 gene data set (see Appendix table). Microarray Suite 5.0 (MAS 5.0, Affymetrix) software was used to generate the array raw data files (CEL files). Then the probe-level data in CEL files were converted into expression measures and normalized by the robust multiarray average algorithm (RMA, www.bioconductor.org).21 The differential expression analysis between low and high BMD samples was conducted by a nonparameter Wilcoxon signed-rank test. A Benjamini and Hochberg (BH) stepwise procedure was used for multiple-comparison adjustment,22 and an adjusted P ≤ .05 was used as the significant criterion. Fisher's exact test was used in the canonical pathway analysis by Ingenuity Pathways Analysis (IPA) software (Ingenuity Systems, www.ingenuity.com) to test the association between genes within a canonical pathway and BMD variation. According to the similarity of gene expression, the differentially expressed genes were further analyzed for two-dimensional hierarchical clustering at both gene and sample levels.23

Array replication in Caucasians

In this independent microarray study, we recruited 20 premenopausal Caucasian women, 10 with high BMD (spine or hip Z-score greater than +0.84) and 10 with low BMD (spine or hip Z-score less than −0.84; see Table 1) from the vicinity of Creighton University in Omaha, Nebraska, USA, for differential expression analyses in their circulating monocytes. Although the weights in the high BMD group were higher than in the low BMD group, no significant correlation of BMD with weight was detected in the low or high BMD group. This study was approved by the Institutional Review Board, and all the subjects signed informed-consent documents before entering the project. The inclusion and exclusion criteria were the same as in the Chinese population, but the age criterion was limited to the narrow range of 39 to 45 years, within the peak BMD range of 20 to 45 years. For the Caucasian samples, we used the Affymetrix HG-133A instead of the HG-U133 plus 2.0 that we used for the Chinese samples (the HG-133A chip contains fewer genes than the HG-U133 plus 2.0), but all the other experimental procedures and statistical analyses were the same as we described for Chinese samples.

Validation by qRT-PCR in Caucasians

We used two-step qRT-PCR to confirm differentially expressed genes. Reverse-transcription reactions were performed in a 50 µL reaction volume containing 5 µL 10× PCR Buffer II, 11 µL 25 mM MgCl2, 10 µL dNTPs, 1.25 µL MULV reverse transcriptase, 1.0 µL RNase inhibitor, 2.5 µL Oligo d(T), 0.5 µg total RNA, and water to 50 µL. All these reagents were supplied by Applied Biosystems (Foster City, CA, USA). Reaction conditions were as follows: 10 minutes at 25°C, 30 minutes at 48°C, and 5 minutes at 95°C. Real-time quantitative PCR was performed in a 25 µL reaction volume using standard protocols on an Applied Biosystems 7900HT. Briefly, 2.5 µL of cDNA was mixed with 12.5 µL of TaqMan universal PCR master mix (2×), 1.25 µL of TaqMan gene express assay mix (contains forward and reverse primers and labeled probe), 1.25 µL of human GAPDH probe (20×), and 7.5 µL of water. The thermocycling conditions were as follows: 2 minutes at 50°C, 10 minutes at 95°C, and 40 cycles of 15 seconds at 95 °C plus 1 minute at 60°C. Based on the relative gene expression 2−ΔΔCt,16 we performed Student's t test to confirm the differential expression genes. All reactions were run in triplicates for each gene.

Confirmation of significant genes in SNP association study

Subjects and phenotype

For the association study, 1000 unrelated Caucasian subjects were identified from our established and expanding genetic repertoire, currently containing more than 6000 subjects. All subjects were U.S. Caucasians of European origin. The inclusion and exclusion criteria were the same as in the Chinese population for expression study, but males and postmenopausal women were included. The basic characteristics of all subjects are listed in Table 1. BMD values at spine and hip were measured using the Hologic 4500A DXA (Hologic, Inc., Bedford, MA, USA). The coefficient of variation of the DXA measurement was approximately 1.98% for spine BMD and 1.87% for hip BMD.

Genotyping and statistical analysis

Genomic DNA was extracted from whole human blood using a commercial isolation kit (Gentra Systems, Minneapolis, MN, USA). Genotyping with the Affymetrix Mapping 250K Nsp and 250K Sty arrays was performed. Fluorescence intensities were quantified using an Affymetrix Array Scanner 30007G. Data management and analyses were performed using the Affymetrix GeneChip Operating System. The final average Bayesian Robust Linear Model with Mahalanobis (BRLMM) call rate across the entire sample reached the high level of 99.14%. We tested the association of significant genes identified in the expression studies with BMD in the 1000 Caucasian subjects. Parameters such as age, age2, sex, age/age2-by-sex interaction, height, and weight were tested for their association with BMD at spine and hip. The significant (P ≤ .05) terms then were included as covariates to adjust the raw BMD values for subsequent analyses. Statistical analyses were performed using genotype and haplotype association software implemented in PLINK-1.03 (http://pngu.mgh.harvard.edu/purcell/plink/).24 Linkage disequilibrium (LD) patterns were analyzed and plotted with the correlation coefficient between pairs of loci based on the 1000 unrelated Caucasians using the the Haploview program (www.broad.mit.edu/mpg/haploview/),25 which describes more or less combinations of alleles. The haplotype block was used to show chromosome regions with high LD and low haplotype diversity in haplotpye association studies. MAPPER was used for searching transcript factor binding sites in the JASPAR database (http://mapper.chip.org/).26

Results

The basic characteristics of the study subjects are shown in Table 1. Although hip BMD is the major study phenotype for the expression analyses in Chinese, spine BMD also was significantly different between the high and low BMD groups (P = 1.49 × 10−6). There were no significant differences in age and height traits between the high and low BMD groups for both Chinese and Caucasians for expression analyses. However, the weight and body mass index (BMI, kg/m2) in the low BMD group were significantly lower than in the high BMD group in Caucasians. We performed a general linear regression analyses for STAT1 expression values and BMD status and incorporated weight and height as covariates in Caucasians. However, weight and height are not significant as covariates for STAT1 expression analysis in the high and low BMD groups (P = .2529 and .2045, respectively). This implied that the weight and height in current study were not confounding factors for BMD in our expression analyses.

All the nominally significant genes (P < .05) of differential expression with BMD in Chinese are summarized in Table 2. We submitted our gene expression profiling to the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/), and the access number was GSE7158. After Benjamini and Hochberg correction for multiple comparisons, the differential expressions of signal transducer and activator of transcription 1 (STAT1) (adjusted P = .02248) and guanylate binding protein 1 (GBP1) (adjusted P = .03372) genes were still significant between the low and the high BMD groups. Expression fold changes of the significant genes were not large in the present study. The main reason might be that the female subjects in this study all were 20 to 45 years of age and with regular menses, in contrast with including both premenopausal and postmenopausal women in our previous study.16 Actually, a 1.5-fold change has been shown to be significant in other differential gene expression studies.27–30 In addition, BMD is a complex trait, and genes regulating its variation are not expected to have large differential expressions. In Fig. 2, a two-dimensional hierarchical dendrogram (based on the nominally significant genes listed in Table 2) shows the results of the hierarchical clustering analyses. Low BMD subjects were mainly clustered to the bottom of the figure.

Table 2. Differential Expression of Cytokine, Chemokines, and Osteoclastogenesis-Related Genes in Blood Monocytes From the Low and the High BMD Groups in Chinese
Probe IDGene symbolGene titleL-BMD intensityH-BMD intensityFold L/HRaw P valueAdjusted P value
  1. Note: Hybridization intensity and “present” status were given based on MAS5 algorithm. L-BMD intensity means hybridization intensity from the low BMD group; H-BMD intensity means hybridization intensity from the high BMD group. Fold L/H means the ratio of hybridization intensity from the low to that from the high BMD group. Raw P value means P value before multiple testing corrections. Adjusted P value represents P value adjusted with Benjamini/Hochberg method, and asterisks * means significant after Benjamini-Hochberg correction considering 281 probes of selected genes.

200887_s_atSTAT1Signal transducer and activator of transcription 1857.21582.341.47.00008.02248*
202269_x_atGBP1Guanylate binding protein 1401.11245.991.63.00024.03372*
204533_atCXCL10Chemokine (C-X-C motif) ligand 10162.4383.221.95.00155.062623
205992_s_atIL15Interleukin 15234.54174.211.35.00310.096789
214453_s_atIFI44Interferon-induced protein 44321.75186.311.73.00693.13910
225636_atSTAT2Signal transducer and activator of transcription 2404.73339.61.19.00937.14628
202688_atTNFSF10Tumor necrosis factor (ligand) superfamily member 101094.65889.051.23.01084.16032
204439_atIFI44LInterferon-induced protein44-like424.81153.592.77.01261.16106
242907_atGBP2Guanylate binding protein 2335.3214.651.56.01677.18849
209417_s_atIFI35Interferon-induced protein 35158.99115.841.37.02209.23874
201642_atIFNGR2Interferon gamma receptor 2831.32811.551.02.02882.27926
212657_s_atIL1RNInterleukin 1 receptor antagonist468.82256.041.83.03721.30425
226757_atIFIT2Interferon-induced protein with tetratricopeptide epeats 2292.64205.91.42.03721.30425
Figure 2.

Two-dimensional hierarchical dendrograms clustered both the rows and columns of the data, the vertical axis showing the clustering of the subjects with different BMD (“L” for the low BMD and “H” for the high BMD) and the horizontal axis showing the clustering of the intensity of different gene expressions.

Many genes in interferon (IFN) pathway were differentially expressed. STAT1, GBP1, interferon gamma receptor 2 (IFNGR2), signal transducer and activator of transcription 2 (STAT2), guanylate binding protein 2 (GPB2), chemokine (C-X-C motif) ligand 10 (CXCL10), interferon-induced protein 44 (IFI44), interferon-induced protein 44-like (IFI44L), tumor necrosis factor (ligand) superfamily member 10 (TNFSF10), interferon-induced protein 35 (IFI35), and interferon-induced protein with tetratricopeptide repeats 2 (IFIT2) genes had higher expression in the low BMD group. Furthermore, the canonical pathway analysis supported the importance of interferon signaling pathway mediated by the STAT1 gene in determining BMD variation (P = 1.67 × 10−9).

In the interleukin system, interleukin 1 receptor antagonist (IL1RN) and interleukin 15 (IL15) genes were upregulated in the low BMD group compared with the high BMD group. However, IL6 gene expression was not detected.

Interestingly, the differential expression of the STAT1 gene in circulating monocytes was replicated in our ongoing comparison microarray study between the low and high BMD premenopausal Caucasian women. Consistently, upregulation of the STAT1 gene in the low BMD group also was significant after Benjamini and Hochberg correction for multiple testing (P = .0028, adjusted P = .048) (Table 3). qRT-PCR confirmed the significant differential expression of the STAT1 gene in Caucasians (P = .0046) (see Table 3). For the GBP1 gene, we did not find any significant results in both array and qRT-PCR analyses in Caucasians.

Table 3. Microarray and qRT-PCR Results for the Expression of STAT1 Gene in Circulating Monocytes Between the Low and High BMD Groups in Chinese and in Caucasians
PopulationStrategyExpression value in low BMDExpression value in high BMDFold change (L/H)P value
  1. Note: Expression value of Affymetrix Microarray was the hybridization intensity based on the MAS5 algorithm; expression value of qRT-PCR was relative quantity based on 2−ΔΔCt; fold L/H means the ratio of gene expression from the low to that from the high BMD group; * indicates the BH-adjusted P values for multiple testing.

ChineseAffymetrix Microarray HG-U133 Plus 2.0857.21 ± 292.94582.34 ± 145.461.47.02248*
CaucasiansAffymetrix Microarray HG-133A957.18 ± 361.28575.67 ± 243.001.66.048*
CaucasiansqRT-PCR3.13 ± 0.841.93 ± 0.751.62.0046

In SNP genotyping analysis using an additive model, two SNPs, rs10199181 (P = .0028) and rs2030171 (P = .0264), in the STAT1 gene were associated with spine BMD (Table 4). It was obvious that subjects with the T allele of rs10199181 possessed high spine BMD (Fig. 3). Figure 4 shows the correlation coefficient between pairs of SNPs of the STAT1 gene and reconstructed haplotype blocks. Interestingly, rs10199181 and rs2030171 were located in the same block, and a haplotype composed of SNPs rs16833157-rs2030171-rs10199181 (“G-G-A”) in the block also was demonstrated to be significantly associated with spine BMD (P = .0029). However, no significant association was detected for 14 SNPs in GBP1 with BMD in Caucasians.

Table 4. Results for Eight SNP Association Analyses of the STAT1 Gene for Hip and Spine BMD in Caucasians
SNP nameSNP IDPositionFunctionAlleleaP HWEbMAFcMAFdP value (hip BMD)P value (spine BMD)
  • N/A = The locus was not calculated for the significant test because of minor allele frequency of < 0.05.

  • a

    The former allele represents the minor allele of each locus.

  • b

    P value for Hardy-Weinberg equilibrium test.

  • c

    Minor allele frequency calculated in our Caucasian sample.

  • d

    Minor allele frequency reported for Caucasians in the public database of HapMap CEU.

SNP_A-1830221rs6718902191546449Intron 24A/G0.72020.2370.229.1171.2828
SNP_A-1966285rs1914408191548221Intron 23A/G10.2350.225.1103.2447
SNP_A-4228696rs34997637191567075Intron 10G/A0.65120.2340.250.7317.0895
SNP_A-2224968rs16833157191570643Intron 9A/G0.35540.0560.034.5069.0672
SNP_A-1966287rs41379347191577187Intron 5G/A10.0080.025N/AN/A
SNP_A-1966288rs2030171191577408Intron 5A/G0.65620.3210.300.5866.0264
SNP_A-4257270rs10199181191581798Intron 4T/A0.72780.3680.342.2602.0028
SNP_A-1783099rs102080331915876625' near geneG/A0.63730.4000.450.8659.5721
Figure 3.

BMD (mean ± SE) in different genotypes of rs10199181 in the STAT1 gene in Caucasians.

Figure 4.

Pairwise linkage disequilibrium pattern of eight SNPs in the STAT1 gene in 1000 unrelated Caucasians. Numbers in the squares are 100 by correlation coefficients (100r2) between pairs of SNPs. The intensity of shading is proportional to r2. SNP IDs in bold represent tag SNPs. Numbers in parentheses indicate lengths of haplotype blocks.

Discussion

In this study we investigated expression of 168 genes related to cytokines, chemokines, osteoclast formation factors, and corresponding receptors in monocytes from Chinese Han women with extremely discordant BMD. Thirteen genes were found to be differentially expressed. A very interesting phenomenon was that among the 13 genes, the STAT1, IFI44L, CXCL10, IFI44, GPB1, and GPB2 genes were expressed in higher levels in the low BMD group than in the high BMD group, which is very similar to the IFN-induced gene expression pattern (IFN pathway). For instance, immature peripheral blood mononuclear phagocytes stimulated by the type I IFN isoform increased the expression of 44 genes, including STAT1, IFI44L, CXCL10, IFI44, GPB1, and GPB2.31 Microarray analysis of cells infected with short hairpin RNA vectors pAB319 and pAB322 showed enhanced expression of many IFN target genes, such as STAT1, IFI44L, CXCL10, IFI44, and GPB1.32 The increased expression of IFN pathway genes also was detected in blood mononuclear cells from patients with systemic lupus erythematosus and juvenile dermatomyositis33 and in the human fibrosarcoma cell line.34 The IFN pathway may regulate bone resorption in two ways. First, interferon-γ (IFNG) blocks RANKL-induced osteoclast differentiation.9, 35 Second, the IFN pathway in circulating monocytes may stimulate the secretion of cytokines IL-1, IL-6, and TNF to increase bone resorption.36–38 In this study, the upregulation of STAT1-mediated IFN pathway genes in the low BMD group suggested the important effect of STAT1 on bone resorption in vivo in humans.

For the 13 differentially expressed genes, however, only the STAT1 and GBP1 genes remained significant after correction for multiple testing. In our previous genome-wide array study on the same Chinese samples, we also found significant differential expression of the STAT1 and GBP1 genes in the array data analyses after correcting for multiple testing.39 However, further qRT-PCR only confirmed the significance of the GBP1 gene but not the STAT1 gene.39 Thus we tried to replicate significance of the two genes in our Caucasian expression study on circulating monocytes from 20 premenopausal Caucasian women (10 with low BMD and 10 with high BMD) and SNP association study on 1000 unrelated Caucasian subjects. We did not find the significance of the GPBP1 gene in either replication study. Interestingly, however, the significance of the STAT1 gene was found in both replication studies. In particular, significant upregulation of the STAT1 gene was found in both array and qRT-PCR experiments in the Caucasian expression study. The STAT1 gene was not differentially expressed in B cells isolated from peripheral blood between the high and low BMD subjects (data not shown) who were the same Caucasians for our current monocyte study. Therefore, it is likely that the alterations in STAT1 expression only in monocytes, but not in other cells, are responsible for variations in bone mass in humans.

In the IFN signaling pathway, STAT1 is a critical mediator gene.9, 40 In the above-mentioned IFN pathway for regulating bone resorption, STAT1 mediates the effects of IFNG on both inhibition of RANKL-induced osteoclast differentiation9, 35, 41 and secretion of IL-1, IL-6, and TNF.36–38 In addition, in dexamethasone-treated peripheral blood mononuclear cell (PBMC) cultures, the inhibited IFNG expression suppressed expression of the STAT1 gene.42 Furthermore, in lupus nephritis patients, basal expression of STAT1 was significantly higher in monocytes. and stimulation of the monocyte cultures with IFNG resulted in phosphorylation of STAT1.43

In mice, the STAT1 gene plays an important role in bone metabolism in osteoblasts.44 Recently, STAT1 was reported to be upregulated in femur tissue in osteoporotic mice,45 and this supports our finding of the high expression of STAT1 in monocytes in human low BMD groups.

Interestingly, our published linkage study of BMD in 4126 human subjects also identified suggestive univariate and significant epistatic linkage signals at 2q32, which harbors the STAT1 gene.46 Furthermore, our group recently found significant linkage evidence on 2q32 with spine BMD using bivariate linkage analysis.47 Our current SNP association study also replicated the significance of the STAT1 gene for spine BMD in Caucasian samples. No SNP in the STAT1 gene was associated with hip BMD at the SNP level, perhaps owing to different genetic determinants for spine BMD and hip BMD traits because many studies have shown different heritability and genetic loci underlying the two traits.47, 48 Hence our results tend to reveal the significance of STAT1 on spine BMD. Subjects with the T allele of SNP rs10199181 in the STAT1 gene tended to have a higher spine BMD than those with other alleles (see Fig. 3). According to the transcript factor Jaspar database, the T allele of rs10199181 is likely to bind transcript factor E4BP4, which might be induced by parathyroid hormone (PTH), a well-know hormone for bone growth, in osteoblasts.49, 50 The inducible effect of E4BP4 suggests a negative regulation by glucocorticoids that might decrease BMD.51 Thus it implies that the T allele of rs10199181 in the STAT1 gene may be involved in bone growth metabolism.

Based on the present results and previous knowledge, we developed a novel mechanism for osteoclastogenesis (Fig. 5). In peripheral blood, IFN mediated by STAT1 may stimulate circulating monocytes to produce cytokines such as IL-1, TNF, CXCL10, and IL-15 that increase the bone resorption function of osteoclasts. Another pathway in the bone microenvironment also may be triggered by upregulated STAT1 and IFN, which may inhibit osteoblatogenesis. In this study, no expression of the RANK and TRAF6 genes in circulating monocytes was detected, suggesting that osteoclast formation was completely inhibited in circulating monocytes. The fact that osteoclast differentiation was not initiated in peripheral circulating monocytes in both the high and low BMD groups is possibly because osteoclast formation from monocytes may occur only in a special microenvironment.52

Figure 5.

Diagram of the osteoclastogenesis mechanism. The novel pathway mediated by STAT1 in blood monocytes was connected by big hollow arrows. The solid arrows indicate activation, and the dashed arrows indicate inhibition.

Our current research studied and found the importance of only the STAT1 gene in the function of monocytes or osteoclasts on bone metabolism, which, however, did not address the reported importance of the STAT1 gene in osteoblasts.9, 44

In summary, our results support the fact that the STAT1 gene in circulating monocytes plays important roles in bone metabolism and also suggests that gene expression of the STAT1-mediated IFN pathway may be important for osteoporosis.

Disclosures

The authors state that they have no conflicts of interest.

Acknowledgements

This study was partially supported by grants from Natural Science Foundation of China (30771222, 30731160618, 30230210, and 30600364) and the Scientific Research Fund of the Hunan Provincial Education Department (04B039, 05B037). HWD was partially supported by grants from the National Institutes of Health (R01 AR050496, K01 AR02170, R01 AR45349, GM60402, R21 AG027110, R21 AA015973, R01 AG026564, and P50 AR055081) and the Dickson/Missouri endowment. This study also was partially supported by grants from the Sate of Nebraska (LB595 and LB692). Both XDC and PX contributed equally to this work.

Appendix Table

Expression of 168 genes (281 probes) Related to Cytokine, Chemokine, and Osteoclastogenesis-Related Genes in Blood Monocytes From the Low and High BMD Groups

Probe IDGene symbolGene titleL-BMD intensityH-BMD intensityFold L/HRaw P valueAdjusted P value
  1. Note: The data were sorted by adjusted P value. Hybridized intensity and “present” status were given based on the MAS5 algorithm. L-BMD intensity means average hybridized intensity in the low BMD group; H-BMD intensity means average hybridized intensity in the high BMD group; Fold L/H means the ration of L-BMD intensity to H-BMD intensity. Raw P value represents P value before multiple testing correction. Adjusted P value represents P value adjusted with Benjamini/Hochberg method; asterisks indicate significant results after Benjamini-Hochberg correction considering 281 probes of selected genes.

200887_s_atSTAT1Signal transducer and activator of transcription 1, 91 kDa857.21582.341.470.000080.02248*
202269_x_atGBP1Guanylate binding protein 1, interferon-inducible, 67 kDa, guanylate binding protein 1, interferon-inducible, 67 kDa401.11245.991.630.000240.03372*
209969_s_atSTAT1Signal transducer and activator of transcription 1, 91 kDa181.8299.561.830.000430.04027*
231577_s_atGBP1Guanylate binding protein 1, interferon-inducible, 67 kDa680363.241.870.000750.052687
207375_s_atIL15RAInterleukin 15 receptor, alpha94.7882.731.150.001560.062623
204533_atCXCL10chemokine (C-X-C motif) ligand 10162.4383.221.950.001550.062623
204533_atCXCL10Chemokine (C-X-C motif) ligand 10162.4383.221.950.001550.062623
202270_atGBP1Guanylate binding protein 1, interferon-inducible, 67 kDa, guanylate binding protein 1, interferon-inducible, 67 kDa518.932881.80.002210.077626
205992_s_atIL15Interleukin 15234.54174.211.350.00310.096789
216598_s_atCCL2Chemokine (C-C motif) ligand 216.088.821.820.003960.11128
222484_s_atCXCL14Chemokine (C-X-C motif) ligand 142.683.60.750.005030.12849
217371_s_atIL15Interleukin 15114.5488.291.30.006930.1391
214453_s_atIFI44Interferon-induced protein 44321.75186.311.730.006930.1391
1560791_atCXCL9Chemokine (C-X-C motif) ligand 98.518.081.050.006920.1391
217502_atIFIT2Interferon-induced protein with tetratricopeptide repeats 2104.4375.691.380.007460.13975
207902_atIL5RAInterleukin 5 receptor, alpha22.3319.241.160.008610.14628
236897_atIL17RBInterleukin 17 receptor, beta3.455.030.690.009330.14628
225636_atSTAT2Signal transducer and activator of transcription 2, 113 kDa404.73339.61.190.009370.14628
202688_atTNFSF10Tumor necrosis factor (ligand) superfamily, member 10, tumor necrosis factor (ligand) superfamily, member 101094.65889.051.230.010840.16032
1560999_a_atIL12RB2Interleukin 12 receptor, beta 27.897.960.990.012470.16106
204439_atIFI44LInterferon-induced protein 44-like424.81153.592.770.012610.16106
205599_atTRAF1TNF receptor-associated factor 120.823.990.870.011680.16106
202748_atGBP2Guanylate binding protein 2, interferon-inducible, guanylate binding protein 2, interferon-inducible307.78240.711.280.013540.16542
227264_atTRAF6TNF receptor-associated factor 631.4331.590.990.015450.18089
242907_atGBP2Guanylate binding protein 2, interferon-inducible335.3214.651.560.016770.18849
209417_s_atIFI35Interferon-induced protein 35158.99115.841.370.022090.23874
204747_atIFIT3Interferon-induced protein with tetratricopeptide repeats 3162.68103.011.580.02360.24561
201642_atIFNGR2Interferon gamma receptor 2 (interferon gamma transducer 1)831.32811.551.020.028820.27926
214038_atCCL8Chemokine (C-C motif) ligand 834.5323.41.480.028680.27926
212657_s_atIL1RNInterleukin 1 receptor antagonist468.82256.041.830.037210.30425
211517_s_atIL5RAInterleukin 5 receptor, alpha18.7119.070.980.032650.30425
204863_s_atIL6STInterleukin 6 signal transducer (gp130, oncostatin M receptor)80.1957.061.410.042150.30425
206693_atIL7Interleukin 728.7332.910.870.037240.30425
206618_atIL18R1Interleukin 18 receptor 111.0121.060.520.047530.30425
221658_s_atIL21RInterleukin 21 receptor11.3413.190.860.039180.30425
237493_atIL22RA2Interleukin 22 receptor, alpha 210.5214.410.730.041770.30425
1552912_a_atIL23RInterleukin 23 receptor5.75.071.120.044640.30425
206569_atIL24Interleukin 2416.9715.761.080.047450.30425
207964_x_atIFNA4Interferon, alpha 415.556.722.310.044680.30425
232375_atSTAT1Signal transducer and activator of transcription 1, 91 kDa142.4389.11.60.047640.30425
226757_atIFIT2Interferon-induced protein with tetratricopeptide repeats 2292.64205.91.420.037210.30425
231578_atGBP1Guanylate binding protein 1, interferon-inducible, 67 kDa20.8315.711.330.047530.30425
210390_s_atCCL14, CCL15Chemokine (C-C motif) ligand 14, chemokine (C-C motif) ligand 152.82.810.990.047640.30425
210549_s_atCCL23Chemokine (C-C motif) ligand 239.6714.940.650.039580.30425
206172_atIL13RA2Interleukin 13 receptor, alpha 23.43.840.890.050170.30837
229263_atIL17RDInterleukin 17 receptor D6.235.421.150.050480.30837
207160_atIL12AInterleukin 12A (natural killer cell stimulatory factor 1, cytotoxic lymphocyte maturation factor 1, p35)4.144.30.960.053550.31472
202411_atIFI27Interferon, alpha-inducible protein 2732.6715.612.090.053760.31472
208164_s_atIL9RInterleukin 9 receptor4.715.640.840.056390.32338
208375_atIFNA1Interferon, alpha 12.85.440.520.064030.34601
1555464_atIFIH1Interferon induced with helicase C domain 170.350.391.40.064030.34601
1569861_atTRAF5TNF receptor-associated factor 56.67.10.930.064030.34601
209687_atCXCL12Chemokine (C-X-C motif) ligand 12 (stromal cell-derived factor 1)5.196.840.760.075780.40178
224283_x_atIL18BPInterleukin 18 binding protein15.3618.890.810.080230.41749
215561_s_atIL1R1Interleukin 1 receptor, type I5.669.410.60.084510.42486
202687_s_atTNFSF10Tumor necrosis factor (ligand) superfamily, member 10, tumor necrosis factor (ligand) superfamily, member 10556.18440.741.260.084670.42486
234516_atIL1R1Interleukin 1 receptor, type I24.2326.620.910.094370.43578
212196_atIL6STInterleukin 6 signal transducer (gp130, oncostatin M receptor)131.01127.641.030.09460.43578
206926_s_atIL11Interleukin 1111.2710.341.090.092680.43578
221926_s_atIL17RCInterleukin 17 receptor C4.334.360.990.093920.43578
205476_atCCL20Chemokine (C-C motif) ligand 2051.069.125.60.094040.43578
210548_atCCL23Chemokine (C-C motif) ligand 233.534.610.770.099490.45091
216243_s_atIL1RNInterleukin 1 receptor antagonist134.2188.041.520.105190.46185
209827_s_atIL16Interleukin 16 (lymphocyte chemoattractant factor)198.96253.610.780.10490.46185
211338_atIFNA2Interferon, alpha 26.316.850.920.11010.47597
205099_s_atCCR1Chemokine (C-C motif) receptor 1381.92370.821.030.116710.4969
205207_atIL6Interleukin 6 (interferon, beta 2)33.8311.2930.129120.49729
204773_atIL11RAInterleukin 11 receptor, alpha35.7737.850.940.128920.49729
222974_atIL22Interleukin 226.785.041.350.129060.49729
220054_atIL23AInterleukin 23, alpha subunit p1918.4522.230.830.12250.49729
207113_s_atTNFTumor necrosis factor411.29195.362.110.122760.49729
208075_s_atCCL7Chemokine (C-C motif) ligand 7, chemokine (C-C motif) ligand 75.288.890.590.128920.49729
1569203_atCXCL2Chemokine (C-X-C motif) ligand 236.7918.621.980.129190.49729
207681_atCXCR3Chemokine (C-X-C motif) receptor 334.9846.840.750.135470.51442
39402_atIL1BInterleukin 1, beta1285.79443.952.90.142680.52501
207906_atIL3Interleukin 3 (colony-stimulating factor, multiple)2.262.211.020.149470.52501
208193_atIL9Interleukin 93.693.141.170.149050.52501
206924_atIL11Interleukin 112.883.680.780.149190.52501
235531_atIL17RBInterleukin 17 receptor B20.8320.611.010.142340.52501
214458_atTRAF3IP1TNF receptor-associated factor 3 interacting protein 111.9811.861.010.148780.52501
64440_atIL17RCInterleukin 17 receptor C79.1786.920.910.157230.52597
229450_atIFIT3Interferon-induced protein with tetratricopeptide repeats 3476.16293.631.620.157230.52597
209774_x_atCXCL2Chemokine (C-X-C motif) ligand 2601.98172.213.50.157230.52597
207850_atCXCL3Chemokine (C-X-C motif) ligand 3201.3780.342.510.157230.52597
202948_atIL1R1Interleukin 1 receptor, type I31.2337.410.830.164480.53815
212659_s_atIL1RNInterleukin 1 receptor antagonist129.83117.411.110.16470.53815
211372_s_atIL1R2Interleukin 1 receptor, type II15.519.10.810.172810.54583
1552915_atIL28AInterleukin 28A (interferon, lambda 2)21.2818.31.160.172880.54583
223710_atCCL26Chemokine (C-C motif) ligand 2619.2117.311.110.172440.54583
205926_atIL27RAInterleukin 27 receptor, alpha142.43145.560.980.181050.55907
204932_atTNFRSF11BTumor necrosis factor receptor superfamily, member 11b, osteoprotegerin (OGP)10.1412.210.830.180010.55907
220971_atIL17EInterleukin 17E10.4212.410.840.189210.56675
219255_x_atIL17RBInterleukin 17 receptor B6.938.360.830.188530.56675
217199_s_atSTAT2Signal transducer and activator of transcription 2, 113 kDa31.5125.831.220.189590.56675
210118_s_atIL1AInterleukin 1, alpha23.5610.892.160.215940.57929
211516_atIL5RAInterleukin 5 receptor, alpha5.928.510.690.215150.57929
202859_x_atIL8Interleukin 81850.81730.412.530.226770.57929
209828_s_atIL16Interleukin 16 (lymphocyte chemoattractant factor)23.0834.550.670.22670.57929
208402_atIL17Interleukin 17 (cytotoxic T-lymphocyte-associated serine esterase 8)14.0219.570.720.20590.57929
221165_s_atIL22Interleukin 2221.8923.560.930.224860.57929
221111_atIL26Interleukin 264.135.10.810.198410.57929
225669_atIFNAR1Interferon (alpha, beta, and omega) receptor 137.9940.960.930.226770.57929
1552611_a_atJAK1Janus kinase 1 (a protein tyrosine kinase)169.58162.821.040.226770.57929
201422_atIFI30Interferon, gamma-inducible protein 303459.223226.641.070.226460.57929
204352_atTRAF5TNF receptor-associated factor 523.8637.430.640.207620.57929
207900_atCCL17Chemokine (C-C motif) ligand 1720.2720.660.980.226620.57929
207445_s_atCCR9Chemokine (C-C motif) receptor 918.7821.370.880.216570.57929
205242_atCXCL13Chemokine (C-X-C motif) ligand 13 (B-cell chemoattractant)2.433.620.670.19780.57929
211469_s_atCXCR6Chemokine (C-X-C motif) receptor 64.676.830.680.215860.57929
203687_atCX3CL1Chemokine (C-X3-C motif) ligand 14.285.340.80.207150.57929
218002_s_atCXCL14Chemokine (C-X-C motif) ligand 142.032.110.960.236090.59767
205067_atIL1BInterleukin 1, beta1436.05513.562.80.257740.62978
210744_s_atIL5RAInterleukin 5 receptor, alpha7.3110.810.680.257330.62978
1552609_s_atIL28A /// IL28BInterleukin 28A (interferon, lambda 2), interleukin 28B (interferon, lambda 3)10.310.990.940.257660.62978
242473_atTRAF4TNF receptor-associated factor 48.110.370.780.257740.62978
243977_atIL6Interleukin 6 (interferon, beta 2)20.9824.020.870.268140.64011
204103_atCCL4Chemokine (C-C motif) ligand 4828.94274.363.020.26880.64011
32128_atCCL18Chemokine (C-C motif) ligand 18 (pulmonary and activation-regulated)13.1415.520.850.268630.64011
207433_atIL10Interleukin 106.197.020.880.291610.67187
212203_x_atIFITM3Interferon induced transmembrane protein 3 (1-8U)1196.83954.471.250.29170.67187
211153_s_atTNFSF11Tumor necrosis factor (ligand) superfamily, member 11, activator of NF-κB ligand (RANKL)4.884.411.110.291370.67187
207794_atCCR2Chemokine (C-C motif) receptor 2, chemokine (C-C motif) receptor 2224.96310.10.730.29170.67187
216244_atIL1RNInterleukin 1 receptor antagonist4.143.791.090.315540.67755
220056_atIL22RA1Interleukin 22 receptor, alpha 125.2121.241.190.302470.67755
208173_atIFNB1Interferon, beta 1, fibroblast3.434.310.80.315210.67755
219209_atIFIH1Interferon induced with helicase C domain 1137.64113.561.210.315710.67755
221571_atTRAF3TNF receptor-associated factor 336.6741.080.890.303540.67755
206983_atCCR6Chemokine (C-C motif) receptor 64.797.610.630.315790.67755
208059_atCCR8Chemokine (C-C motif) receptor 83.423.910.870.315540.67755
204470_atCXCL1Chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity, alpha)64.1828.142.280.315790.67755
206336_atCXCL6Chemokine (C-X-C motif) ligand 6 (granulocyte chemotactic protein 2)6.949.410.740.315870.67755
207072_atIL18RAPInterleukin 18 receptor accessory protein32.170.390.460.328280.68858
216020_atIFIH1Interferon induced with helicase C domain 110.337.561.360.328360.68858
207861_atCCL22Chemokine (C-C motif) ligand 223.94.210.930.327280.68858
207037_atTNFRSF11ATumor necrosis factor receptor superfamily, member 11a, activator of NF-κB, receptor activator of nuclear factor κB (RANK)12.0113.750.870.340920.69503
205114_s_atCCL3, CCL3L1Chemokine (C-C motif) ligand 3, chemokine (C-C motif) ligand 3-like 11514.49513.722.950.341330.69503
221463_atCCL24Chemokine (C-C motif) ligand 244.127.670.540.3410.69503
208304_atCCR3Chemokine (C-C motif) receptor 355.8868.990.810.341330.69503
228977_atIL17DInterleukin 17D3.924.040.970.354050.69653
205707_atIL17RInterleukin 17 receptor296.73357.670.830.354460.69653
208261_x_atIFNA10Interferon, alpha 1012.6614.890.850.354460.69653
208448_x_atIFNA16Interferon, alpha 162019.591.020.354130.69653
1562296_atCXCL14Chemokine (C-X-C motif) ligand 1410.647.691.380.354460.69653
211506_s_atIL8Interleukin 81343.34417.693.220.368070.69884
1552584_atIL12RB1Interleukin 12 receptor, beta 181.7188.50.920.368070.69884
208548_atIFNA6Interferon, alpha 625.5529.90.850.367410.69884
207354_atCCL16Chemokine (C-C motif) ligand 167.338.090.910.367660.69884
204606_atCCL21Chemokine (C-C motif) ligand 2113.189.831.340.367740.69884
224079_atIL17CInterleukin 17C35.9536.440.990.395340.7044
219115_s_atIL20RAInterleukin 20 receptor, alpha4.774.7810.381170.7044
219971_atIL21RInterleukin 21 receptor6.058.490.710.395820.7044
208344_x_atIFNA13Interferon, alpha 1324.6814.861.660.381420.7044
203153_atIFIT1Interferon-induced protein with tetratricopeptide repeats 1, interferon-induced protein with tetratricopeptide repeats 1199.61122.271.630.396070.7044
201601_x_atIFITM1Interferon induced transmembrane protein 1 (9-27)371.78359.831.030.396070.7044
205558_atTRAF6TNF receptor-associated factor 640.1251.70.780.381010.7044
224027_atCCL28Chemokine (C-C motif) ligand 2812.0512.170.990.39590.7044
203666_atCXCL12Chemokine (C-X-C motif) ligand 12 (stromal cell-derived factor 1)47.9155.580.860.395580.7044
237038_atCXCL14Chemokine (C-X-C motif) ligand 144.895.250.930.380440.7044
205291_atIL2RBInterleukin 2 receptor, beta, interleukin 2 receptor, beta90.85134.180.680.425320.7114
206148_atIL3RAInterleukin 3 receptor, alpha (low affinity)33.4229.591.130.425160.7114
211000_s_atIL6STInterleukin 6 signal transducer (gp130, oncostatin M receptor)42.9941.691.030.425240.7114
212195_atIL6STInterleukin 6 signal transducer (gp130, oncostatin M receptor)333.64330.731.010.425320.7114
207901_atIL12BInterleukin 12B (natural killer cell stimulatory factor 2, cytotoxic lymphocyte maturation factor 2, p40)13.5815.130.90.410060.7114
1552610_a_atJAK1Janus kinase 1 (a protein tyrosine kinase)195.54234.770.830.425240.7114
238494_atTRAF3IP1TNF receptor-associated factor 3 interacting protein 142.0848.730.860.424360.7114
206988_atCCL25Chemokine (C-C motif) ligand 259.389.4110.424680.7114
224240_s_atCCL28Chemokine (C-C motif) ligand 2823.7830.340.780.4250.7114
1568934_atCX3CR1Chemokine (C-X3-C motif) receptor 125.2437.660.670.4250.7114
201887_atIL13RA1Interleukin 13 receptor, alpha 1267.86291.220.920.455710.72771
1561853_a_atIL23RInterleukin 23 receptor4.614.551.010.454630.72771
242903_atIFNGR1Interferon gamma receptor 1265.562511.060.455790.72771
214022_s_atIFITM1Interferon induced transmembrane protein 1 (9-27)593.06624.520.950.455790.72771
208315_x_atTRAF3TNF receptor-associated factor 366.3870.170.950.45540.72771
206978_atCCR2Chemokine (C-C motif) receptor 2, chemokine (C-C motif) receptor 2556.27705.50.790.455790.72771
207852_atCXCL5Chemokine (C-X-C motif) ligand 512.516.090.780.455710.72771
206974_atCXCR6Chemokine (C-X-C motif) receptor 610.8815.490.70.45540.72771
217119_s_atCXCR3Chemokine (C-X-C motif) receptor 310.8421.840.50.47110.7479
211405_x_atIFNA17Interferon, alpha 1733.1636.390.910.487010.76882
217489_s_atIL6RInterleukin 6 receptor35.9745.580.790.520270.7818
214950_atIL9RInterleukin 9 receptor10.4321.450.490.51970.7818
201888_s_atIL13RA1Interleukin 13 receptor, alpha 1107.98114.140.950.52020.7818
219323_s_atIL18BPInterleukin 18 binding protein11.1718.060.620.520060.7818
1552995_atIL27Interleukin 277.785.661.380.503360.7818
244261_atIL28RAInterleukin 28 receptor, alpha (interferon, lambda receptor)11.3917.450.650.520270.7818
210643_atTNFSF11Tumor necrosis factor (ligand) superfamily, member 11, activator of NF-κB ligand (RANKL)7.7511.010.70.519980.7818
206991_s_atCCR5Chemokine (C-C motif) receptor 553.2798.450.540.52020.7818
205898_atCX3CR1Chemokine (C-X3-C motif) receptor 11343.171679.710.80.520270.7818
220273_atIL17BInterleukin 17B3.016.060.50.536680.79372
224071_atIL20Interleukin 2022.9324.020.950.536050.79372
1555499_a_atIL28RAInterleukin 28 receptor, alpha (interferon, lambda receptor)23.7822.541.060.536680.79372
207538_atIL4Interleukin 49.7510.110.960.554120.80688
204912_atIL10RAInterleukin 10 receptor, alpha681.13656.721.040.554190.80688
223030_atTRAF7TNF receptor-associated factor 713.4418.420.730.554050.80688
205403_atIL1R2Interleukin 1 receptor, type II36.2548.670.740.589150.81153
234967_atIL6STInterleukin 6 signal transducer (gp130, oncostatin M receptor)3.653.371.080.588640.81153
1552646_atIL11RAInterleukin 11 receptor, alpha46.7358.540.80.569560.81153
222062_atIL27RAInterleukin 27 receptor, alpha141.68184.70.770.589150.81153
204191_atIFNAR1Interferon (alpha, beta, and omega) receptor 128.9725.981.120.586390.81153
206332_s_atIFI16Interferon, gamma-inducible protein 16742.15756.260.980.589150.81153
211899_s_atTRAF4TNF receptor-associated factor 48.439.490.890.570620.81153
223029_s_atTRAF7TNF receptor-associated factor 739.6531.971.240.571410.81153
210072_atCCL19Chemokine (C-C motif) ligand 1920.5819.631.050.588960.81153
207955_atCCL27Chemokine (C-C motif) ligand 2729.4933.850.870.58890.81153
211122_s_atCXCL11Chemokine (C-X-C motif) ligand 1117.698.562.070.571210.81153
205098_atCCR1Chemokine (C-C motif) receptor 1514.24489.351.050.606950.83197
204116_atIL2RGInterleukin 2 receptor, gamma (severe combined immunodeficiency)193.16255.560.760.625050.83249
205945_atIL6RInterleukin 6 receptor, interleukin 6 receptor258.64261.180.990.625050.83249
205798_atIL7RInterleukin 7 receptor, interleukin 7 receptor64.86104.790.620.625110.83249
206295_atIL18Interleukin 18 (interferon-γ-inducing factor)67.1676.060.880.625110.83249
208965_s_atIFI16Interferon, gamma-inducible protein 16347.81327.781.060.625110.83249
1555759_a_atCCL5Chemokine (C-C motif) ligand 5972.23900.041.080.625110.83249
204864_s_atIL6STInterleukin 6 signal transducer (gp130, oncostatin M receptor)29.5330.20.980.661860.84938
221947_atIL17RCInterleukin 17 receptor C47.8844.711.070.661750.84938
217702_atIL27RAInterleukin 27 receptor, alpha14.9312.411.20.643370.84938
214569_atIFNA5Interferon, alpha 56.936.71.030.660830.84938
1553574_atIFNE1Interferon epsilon 115.1314.111.070.660990.84938
201648_atJAK1Janus kinase 1 (a protein tyrosine kinase)543.47603.10.90.661970.84938
204413_atTRAF2TNF receptor-associated factor 23.84.760.80.66170.84938
214974_x_atCXCL5Chemokine (C-X-C motif) ligand 5529.1524.141.010.661970.84938
207849_atIL2Interleukin 26.566.191.060.680470.85388
208259_x_atIFNA7Interferon, alpha 726.1317.841.460.680570.85388
208182_x_atIFNA14Interferon, alpha 1427.8734.380.810.679640.85388
202727_s_atIFNGR1Interferon gamma receptor 11046.251094.150.960.680670.85388
210163_atCXCL11Chemokine (C-X-C motif) ligand 1117.348.81.970.680410.85388
226333_atIL6RInterleukin 6 receptor370.68448.760.830.699680.86232
1555016_atIL16Interleukin 16 (lymphocyte chemoattractant factor)37.2636.321.030.699140.86232
204933_s_atTNFRSF11BTumor necrosis factor receptor superfamily, member 11b, osteoprotegerin (OGP)6.464.641.390.699680.86232
206337_atCCR7Chemokine (C-C motif) receptor 7, chemokine (C-C motif) receptor 757.8865.730.880.699680.86232
207008_atIL8RBInterleukin 8 receptor, beta179.1225.480.790.718770.87815
206890_atIL12RB1Interleukin 12 receptor, beta 134.1645.320.750.718770.87815
206999_atIL12RB2Interleukin 12 receptor, beta 215.3818.640.830.737920.88263
234408_atIL17FInterleukin 17F2.322.440.950.737530.88263
201315_x_atIFITM2Interferon induced transmembrane protein 2 (1-8D)1306.871373.990.950.738140.88263
205392_s_atCCL14, / CCL15Chemokine (C-C motif) ligand 14, chemokine (C-C motif) ligand 1512.3615.560.790.737220.88263
217028_atCXCR4Chemokine (C-X-C motif) receptor 4438.44468.810.940.738140.88263
211269_s_atIL2RAInterleukin 2 receptor, alpha24.3926.680.910.757380.89052
221271_atIL21Interleukin 216.9370.990.757260.89052
209924_atCCL18Chemokine (C-C motif) ligand 18 (pulmonary and activation-regulated)18.1816.691.090.757260.89052
203915_atCXCL9Chemokine (C-X-C motif) ligand 942.8436.421.180.757420.89052
216876_s_atIL17Interleukin 17 (cytotoxic T-lymphocyte-associated serine esterase 8)2.992.741.090.777150.90614
235116_atTRAF1TNF receptor-associated factor 126.1931.30.840.77670.90614
210133_atCCL11Chemokine (C-C motif) ligand 1113.5917.240.790.796980.92161
208376_atCCR4Chemokine (C-C motif) receptor 421.0620.581.020.796710.92161
217212_s_atIL9RInterleukin 9 receptor55.7861.460.910.81690.92938
214059_atIFI44Interferon-induced protein 4467.7953.631.260.816930.92938
230327_atCCL27Chemokine (C-C motif) ligand 2717.4815.391.140.816810.92938
823_atCX3CL1Chemokine (C-X3-C motif) ligand 121.6219.671.10.816810.92938
207539_s_atIL4Interleukin 419.6818.361.070.857040.94123
203233_atIL4RInterleukin 4 receptor120.73114.061.060.857140.94123
209575_atIL10RBInterleukin 10 receptor, beta279.27295.770.940.897690.94123
211612_s_atIL13RA1Interleukin 13 receptor, alpha 1, interleukin 13 receptor, alpha 1150.14153.820.980.897620.94123
227401_atIL17DInterleukin 17 receptor D8.67.621.130.897580.94123
224156_x_atIL17RBInterleukin 17 receptor B18.0813.811.310.877180.94123
224361_s_atIL17RBInterleukin 17 receptor B10.4816.90.620.897530.94123
227997_atIL17RDInterleukin 17 receptor D27.2732.840.830.897620.94123
236186_x_atIL17REInterleukin 17 receptor E2.383.130.760.857020.94123
222868_s_atIL18BPInterleukin 18 binding protein56.1258.320.960.877290.94123
222829_s_atIL20RAInterleukin 20 receptor, alpha4.783.861.240.877310.94123
1552917_atIL29Interleukin 29 (interferon, lambda 1)33.8337.220.910.897690.94123
204786_s_atIFNAR2Interferon (alpha, beta, and omega) receptor 2124.38130.380.950.897650.94123
211676_s_atIFNGR1Interferon gamma receptor 1, interferon gamma receptor 1479.53517.040.930.857140.94123
205170_atSTAT2Signal transducer and activator of transcription 2, 113 kDa47.3546.011.030.836970.94123
208966_x_atIFI16Interferon, gamma-inducible protein 16591.69599.190.990.857140.94123
238846_atTNFRSF11ATumor necrosis factor receptor superfamily, member 11a, activator of NF-κB, receptor activator of nuclear factor κB (RANK)18.5319.760.940.856940.94123
207533_atCCL1Chemokine (C-C motif) ligand 12.383.420.690.89760.94123
206407_s_atCCL13Chemokine (C-C motif) ligand 133.596.210.580.836860.94123
215101_s_atCXCL5Chemokine (C-X-C motif) ligand 595.1396.570.990.897690.94123
211919_s_atCXCR4Chemokine (C-X-C motif) receptor 4, chemokine (C-X-C motif) receptor 4318.92339.410.940.897670.94123
209201_x_atCXCR4Chemokine (C-X-C motif) receptor 4305.74310.980.980.918050.959
210904_s_atIL13RA1Interleukin 13 receptor, alpha 1117.07128.580.910.93850.97313
1405_i_atCCL5Chemokine (C-C motif) ligand 51312.381268.121.030.93850.97313
202871_atTRAF4TNF receptor-associated factor 418.6817.181.090.958970.98707
223454_atCXCL16Chemokine (C-X-C motif) ligand 16108.07104.491.030.958940.98707
207952_atIL5Interleukin 5 (colony-stimulating factor, eosinophil)10.7113.630.790.979450.99721
207844_atIL13Interleukin 1318.9732.380.590.979470.99721
224514_x_atIL17RCInterleukin 17 receptor C, interleukin 17 receptor C29.2836.30.810.979440.99721
206341_atIL2RAInterleukin 2 receptor, alpha12.0515.510.7811
226218_atIL7RInterleukin 7 receptor62.02108.450.5711
220745_atIL19Interleukin 1925.6629.860.8611
225661_atIFNAR1Interferon (alpha, beta, and omega) receptor 176.4682.390.9311
204785_x_atIFNAR2Interferon (alpha, beta, and omega) receptor 2116.43124.340.9411

Ancillary