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Rheumatology Advance Access originally published online on January 17, 2006
Rheumatology 2006 45(6):694-702; doi:10.1093/rheumatology/kei244
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© The Author 2006. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Signatures of differentially regulated interferon gene expression and vasculotrophism in the peripheral blood cells of systemic sclerosis patients

F. K. Tan, X. Zhou, M. D. Mayes, P. Gourh, X. Guo, C. Marcum, L. Jin1 and F. C. Arnett, Jr

Division of Rheumatology, Department of Internal Medicine, UT Houston Medical School, Houston, TX and 1 Center for Genome Information, University of Cincinnati, Cincinnati, OH, USA.

Correspondence to: F. K. Tan, Division of Rheumatology, Department of Internal Medicine, UT Houston Medical School, 6431 Fannin, MSB 5.270, Houston, TX 77030, USA. E-mail: filemon.k.tan{at}uth.tmc.edu


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 References
 
Objective. To obtain a global view of the immunological alterations occurring in early systemic sclerosis (SSc) by transcriptional profiling of peripheral blood cells (PBCs).

Methods. Oligonucleotide microarrays were used to compare PBC gene expression profiles in 18 SSc cases (<2 yr duration) and 18 controls matched for race, gender and ethnicity. SSc cases had no prior or current exposure to cytotoxic drugs. PAXgene tubes were used to stabilize RNA during phlebotomy. Changes in gene expression were independently validated by real-time polymerase chain reaction.

Results. SSc PBCs demonstrated differential expression of 18 interferon-inducible genes. Six of these genes were identical to the interferon signature genes in lupus peripheral blood mononuclear cells. Notably, SSc PBCs also had increased expression of allograft inflammatory factor (AIF1) and several selectins and integrins involved in cellular adhesion to the endothelium. Global analysis of 284 known biological pathways revealed that 13 were differentially regulated in SSc PBCs, including two pathways (IL2RB and GATA3) that lead to TH2 polarization.

Conclusions. Transcriptional profiling reliably discriminates between PBCs from SSc and normal donors despite the fact that they represent a heterogeneous cell population. Multiple biological pathways were differentially regulated in SSc PBCs, but a common thread across these pathways was alterations in protein tyrosine kinase 2ß and mitogen-activated protein kinase signalling. Although the SSc PBC gene expression profile demonstrated some parallels with the lupus interferon gene signature, there was also increased expression of transcripts encoding proteins that target PBCs to the endothelium, which might be relevant to the vasculopathy of SSc.

KEY WORDS: Autoimmunity, Systemic sclerosis, Microarrays, Blood cells, Human


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 References
 
Endothelial cell dysfunction and immune dysregulation can be viewed as sentinel events in the pathophysiology of systemic sclerosis (SSc) because they often precede the fibrosis that is the most widely recognized clinical feature of this disease [1]. Raynaud's phenomenon and nailfold capillary abnormalities are some of the earliest clinical signs of vascular pathology in SSc, while intimal proliferation and vascular obliteration occur in later stages of the disease [2]. In gene profiling studies of dermal fibroblasts, important effector cells involved in the pathogenesis of SSc, we found that one of the most discriminating features of SSc fibroblasts was the increased expression of transcripts for collagen XVIII{alpha}1 (COL18A1), whose C-terminal fragment contains endostatin, a potent inhibitor of angiogenesis and endothelial proliferation. This was accompanied by decreased expression of vascular endothelial growth factor B (VEGFB) compared with normal fibroblasts, thus raising the possibility that the SSc fibroblast might contribute to the microvascular abnormalities seen in SSc [3].

Dysregulation of humoral immunity in SSc is best represented by the variety of autoantibodies to nuclear and nucleolar components spontaneously produced by SSc patients, some of which are highly disease-specific. Strong correlations exist between certain SSc-specific autoantibodies and clinical subtypes of disease [4]. In addition, recent reports suggest that anti-topoisomerase antibodies directly bind fibroblasts, and that their serum levels correlate with SSc disease activity [5, 6]. These studies, together with observations in systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), indicate that disease-specific autoantibodies precede the first clinical signs of disease by several years, providing strong circumstantial evidence suggesting that autoantibodies might be pathogenic [7, 8].

Abnormalities in cellular immunity are apparent from the increased numbers of T cells bearing markers of activation in the peripheral blood of SSc patients [9]. In the early stages of SSc, there is a prominent dermal mononuclear infiltrate, often found in a perivascular distribution [2]. It has been shown that the T cells infiltrating the skin and the lungs of SSc patients have a restricted T-cell receptor repertoire [10–12]. In studies of bronchoalveolar lavage fluids, the TH2 cytokine pattern, mainly IL-4, has been associated with worsening forced vital capacity [13, 14].

In this report, we outline the results of transcriptional profiling of peripheral blood cells (PBCs) from patients with early SSc. Our hypothesis is that since immune dysregulation is an early event in SSc, analysis of global gene expression patterns of these effector cells might provide novel perspectives into its pathogenesis. Because substantial changes in gene expression have been well documented in blood cells due to ex vivo handling, we used a commercially available method for bedside RNA stabilization to give a more accurate reflection of the PBC gene expression in vivo [15–17].


   Methods
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 Abstract
 Introduction
 Methods
Results
 Discussion
 References
 
Study sample
All study subjects provided written informed consent and the study was approved by the University of Texas-Houston Committee for the Protection of Human Subjects. The clinical characteristics of the patients are shown in Table 1. All SSc patients were in the early, oedematous phase of skin involvement and none of the patients was on any cytotoxic therapy at the time of phlebotomy, although seven patients were on low-dose prednisone (5–10 mg per day). Blood was drawn by phlebotomy directly into PAXgeneTM tubes (Qiagen, Valencia, CA, USA) and all samples were handled in a standardized fashion according the manufacturer's instructions. Total RNA was extracted from 5 ml whole blood using the PAXgene Blood RNA Kit (Qiagen). RNA quality was assessed using A260/A280 ratios and the presence of intact 28S and 18S bands on agarose gel electrophoresis.


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  		TABLE 1. Clinical and serological characteristics of SSc and healthy PBC donors

 
Microarrays
A reference experimental design was used for these studies (Human Universal Reference RNA, Stratagene, La Jolla, CA, USA). Since class comparison and class prediction were the primary aims, we favoured studying larger numbers of biological samples rather than fewer samples with multiple technical replicates [18]. The microarray used is a custom-printed array representing 16 659 human genes from the Qiagen Array-Ready Oligo Set version 1.1. The 3DNA Array 900 Detection Kit (Genisphere, Montvale, NJ, USA) was used to synthesize labelled probes from 1 µg of peripheral blood mononuclear cell (PBMC) total RNA (Cy5) or reference RNA (Cy3), and hybridization and washes were carried out according to the vendor's protocol. The features were extracted from the TIFF files using fixed-circle segmentation (QuantArray v. 3.1; Perkin-Elmer) and preprocessed as previously described [3].

Features with a poor signal-to-noise ratio (<3) or that were obvious artefacts were flagged for exclusion. To filter out non-expressed genes, we used the t-test to compare the signal for each gene with the signals for 30 negative control features on the array across all experiments for each channel. Any gene whose signal was not significantly different from the negative control (P>0.05) was excluded. In addition, any gene in which the expression data had been filtered out in 20% of the experiments was also excluded from further analysis. The signal intensities from both channels were transformed into log2 ratios and local weighted least squares regression or lowess normalization was used to adjust for differences in labelling intensities of the two fluorescent dyes [19]. The program BRB ArrayTools v. 3.2, designed by Richard Simon and Amy Peng Lam (Biometric Research Branch, National Cancer Institute, USA), was used for data analysis [3]. This program implements a variety of analytical and data mining methods, including classification, class prediction, quantitative trait analysis [20], significance analysis of microarrays (SAM) [21], and comparative gene ontology and pathway analysis. To estimate the actual number of false discoveries at a given significance level, we used the spacings locally weighted regression smoother histogram (SPLOSH) to calculate the conditional false discovery rate (cFDR) at each significance level [22].

Quantitative real-time polymerase chan reaction
The quantitative real-time polymerase chain reaction (real-time PCR) was performed to confirm microarray results. Real-time PCR was performed using validated TaqMan® Gene Expression Assays on an Applied Biosystems 7900HT Fast Real-Time PCR System (Applied Biosystems, CA, USA). cDNA was synthesized using the High-Capacity cDNA Archive Kit from Applied Biosystems. The transferrin receptor gene (TFRC/CD71) was used as an endogenous control to normalize transcript levels of total RNA of each sample. The data were analysed with SDS 2.2 software using the comparative CT method (2-ddCT method). Fold change was calculated as 2–{Delta}{Delta}CT. The Mann–Whitney U-test was used to assess significant differences in transcript levels.


   Results
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 Abstract
 Introduction
 Methods
 Results
Discussion
 References
 
Global gene expression profiles from SSc and normal donors are significantly different
A total of 8541 genes (51%) passed the filtering and preprocessing criteria (see Methods) when all 36 samples were considered together. This is comparable to the percentage present calls in methods that use globin reduction protocols prior to linear RNA amplification (Affymetrix Technical Note; http://www.affymetrix.com/support/technical/technotes/blood2_technote.pdf).

We initially screened for differentially regulated genes using SAM and found 504 (5.9%) genes whose expression level was significantly different between SSc and normal PBCs. To further refine the gene list, we used the random variance t-test to discover differentially regulated genes, and SPLOSH to estimate the cFDR at each significance level. By restricting the cFDR rate to ≤10%, we found that 382 genes (4.5%) out of 8541 expressed genes were potentially differentially regulated in SSc PBCs compared with normal PBCs. This included 244 genes whose transcripts were significantly increased and 138 genes that were significantly decreased in SSc PBCs. Although the fold changes were modest (of the order of 2- to 3-fold), they were highly statistically significant, ranging from P = 0.0059 to P = 5 x 10–7. The global probability of finding 382 genes differentially expressed by chance alone if there was no real difference between SSc and normal PBCs is less than 1 in 1000 (P = 0.0007), suggesting that these genes are likely to be biologically relevant to SSc.

We then performed unsupervised hierarchical cluster analysis of all the samples with these 382 genes (Fig. 1). There was a distinct separation of the 32 SSc and normal PBMC samples into two major groups, although four of the samples (two controls and two SSc patients) were misclassified based on this gene set. The cluster demonstrated very good reproducibility, having a robustness index after 1000 permutations of R = 0.961 [23]. Figure 1 shows that two large clusters of co-regulated genes (clusters 37 and 71) separated the SSc and normal PBCs. Cluster 37 contains 73 genes overexpressed in SSc PBCs, and they include: SELL, SELPLG, ITGB2 (CD18), S100A12, S100A9 and CFLAR as well as several interferon (IFN)-inducible genes (IFI30, STAT1, TAP1, ISG20 and IFNGR2). Smaller clusters that are also overexpressed in the SSc samples include cluster 15, which contains another group of three IFN-related genes, and cluster 27, which contains S100A8, CD52 and AIF1 (Fig. 1). Cluster 71 contains 40 genes significantly underexpressed by SSc PBCs, including ADAM9, EGFR, ZNF259, FGF4, CTGF and several transcription factors (NFYA, NR3C1, POU3F1 and RB1). Quantitative differences in gene expression between SSc and normal PBCs for selected genes are shown in Table 2 and a comprehensive list is publicly available at http://www.uth.tmc.edu/scleroderma.


Figure 1
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  		FIG. 1. Hierarchical clustering of genes and samples. The gene panel used comprised the 382 genes differentially expressed at a conditional false discovery rate of ≤0.10 (P<0.006). Centred correlation metric and average linkage clustering was used. The genes constituting several clusters of interest (clusters 15, 27, 37, 64 and 71) are shown to the right of the heatmap. The complete list of genes that constitute each gene cluster is available at http://www.uth.tmc.edu/scleroderma.

 

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  		TABLE 2. Selected genes differentially expressed by SSc PBCs ranked by fold difference within each groupa

 
Multiple interferon-inducible genes and biological pathways are differentially regulated in SSc PBCs
Closer examination of the data shows that SSc PBCs have significantly increased expression of at least seven type I IFN-inducible genes, including G1P3, G1P2, MNDA, IRF7, TAP1, ISG20 and MX1 (Table 2). In addition, there was also differential expression genes typically activated by IFN-{gamma}, a type II IFN (IFI30, IFNGR2, HLA-E, SLC11A1 and S100A8) (Table 2). In contrast, SOCS3, an important negative regulator of IFN-{gamma} signalling, was significantly decreased in SSc PBCs, while STAT1, which is involved in both type I and type II IFN signalling, was significantly increased in SSc PBCs.

Next, using BRB Array Tools, we examined known biological pathways in order to discover those that might be differentially regulated between SSc and control PBCs. The results are summarized in Table 3. Thirteen out of 284 biological pathways in publicly available (KEGG and BioCarta) databases were found to be differentially regulated in SSc PBCs. It should be pointed out that these pathways are not mutually exclusive and some of the individual genes were represented in more than one biological pathway. The complete list of genes constituting these pathways is available at http://www.uth.tmc.edu/scleroderma. The most significantly differentially regulated pathway was the Pyk2 pathway (P = 1 x 10–5), with eight of 15 genes showing differential expression. These included increased expression of GRB2 (1.5-fold, P = 0.0007), PTK2B (1.40-fold, P = 0.007), RAF1 (1.6-fold, P = 0.006), MAPK14 and MAP2K3, and decreased expression of RBL2 (1.4-fold, P = 0.01), CRKL (1.5-fold, P = 0.008) and MAP2K4 (1.3-fold, P = 0.007).


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  		TABLE 3. Pathways which discriminate SSc and normal PBCs sorted by significancea

 
The IL-2RB pathway (P = 0.0043) contains the largest number of genes among the differentially regulated pathways, with 23 genes in all (Table 3). These included increased expression of CFLAR (1.8-fold, P = 0.0007), GRB2, RAF1 and RPS6KB1 (1.4-fold, P = 0.02) and decreased expression of SOCS3, IRS1 (1.6-fold, P = 0.002), and CRKL. In another pathway for TH2 activation, GATA3 (P = 0.002), three of five genes were significantly differentially regulated, including JUNB (1.4-fold, P = 0.002), MAPK14 (1.4-fold, P = 0.004) and MAP2K3 (1.6-fold, P = 0.0008).

Independent validation of array data with real-time PCR
We then selected 11 genes differentially regulated on the microarray between SSc and normal PBCs for independent validation by real-time PCR (Fig. 2). Four of these genes, ITGA2B, G1PBB, SELL and AIF1, code for proteins that are important for endothelial cell adhesion, while IRF7, G1P3 and S100A8 are IFN-inducible genes. Overall, the real-time PCR data showed good concordance with the microarray results (Fig. 2).


Figure 2
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  		FIG. 2. Comparative expression levels of genes in SSc vs controls by real-time PCR. Real-time PCR of 11 genes selected from microarray data was performed on cDNA of SSc patients and controls. The box plots above show the normalized transcript levels and the median is indicated by the horizontal line. The error bars indicate the 90th and 10th percentiles, and the boxed areas indicate the 25th and 75th percentiles. The dots represent the highest and lowest expression levels for that gene. The Mann–Whitney U-test was used to assess the differences for statistical significance.

 
Lack of correlation between gene expression patterns and clinical parameters
We performed a subanalysis of the SSc patients using with SAM and the random variance t-test to detect any genes that may be correlated with clinical features. We were unable to detect any genes that correlated with modified Rodnan skin score, anti-topoisomerase antibody status, interstitial lung disease, or low dose prednisone use.


   Discussion
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 Abstract
 Introduction
 Methods
 Results
 Discussion
References
 
To our knowledge, this is the first report of transcriptional profiling of PBCs in SSc patients. Previous studies have been carried out in PBMCs in SLE, RA, ANCA-associated glomerulonephritis and multiple sclerosis [24–28]. However, there is a significant trade-off with the use of purified cells in that substantial changes in gene expression have been documented in PBCs with ex vivo handling [15, 16]. Indeed, Baechler et al. [15] has reported that out of 4566 genes expressed in PBMCs, 2034 undergo significant changes in expression under environmental stress, and these had to be excluded from analysis [24]. By using a commercially available method to immediately stabilize blood RNA upon phlebotomy, we could better preserve the immunological alterations that are present in vivo, with minimal interference from artefacts that arise from blood handling, storage and separation [17]. The disadvantage of this approach is that once the whole blood is stabilized it is not possible to subsequently separate the different cell populations. However, we felt that this was a reasonable compromise, given that the present approach makes sample collection practical in a busy out-patient clinical setting, and data retention is maximized by immediately stabilizing the blood RNA at bedside.

Our results show that transcriptional profiling can reliably discriminate between PBCs from SSc and normal donors despite the fact that PBCs are a heterogeneous cell population of lymphocytes, neutrophils, monocytes, macrophages, NK cells, dendritic cells and mast cells. These differences clearly highlight the signatures of immunological dysregulation of SSc and provide a framework for subsequent investigations into disease mechanisms or biomarkers. These data, taken together with our previous observations of increased expression of COL18A1, and decreased expression of VEGFB in cultured, non-lesional dermal fibroblasts from patients with early diffuse SSc, suggest intriguing mechanisms in which the immune system and the fibroblasts can each uniquely contribute to the vasculopathy of SSc [3].

Relevant to the vasculopathy of SSc is that PBCs from SSc patients have increased expression of a set of genes that appear to target these cells to the endothelium. These include GP1BB, ITGA2B, AIF1, SELL, SELPLG, ITGB2, ITGA6 and ITGB5 [29, 30]. SELL and ITGA6 encode key leucocyte adhesion molecules involved in initial tethering to the endothelial cell. SELPLG, a major ligand for selectin-P, has been shown to be involved in leucocyte rolling on endothelial cells and the homing of T lymphocytes to the skin. On the other hand, ITGA2B and GP1BB are receptors for von Willebrand factor (vWF) which have been shown to be abnormally expressed in SSc [31–33]. AIF1 is a gene previously reported to be expressed by macrophages in response to various cytokines (e.g. IFN-{gamma}, TGF-ß), and in response to vascular injury it enhances vascular smooth muscle cell migration and intimal hyperplasia [34, 35]. Differential regulation of multiple transcripts involved in the actin cytoskeleton was also noted. Indeed, a common thread among all the differentially regulated pathways in Table 3 is protein tyrosine kinase 2ß and/or mitogen-activated protein kinase (MAP kinase) signalling. These genes play a critical role in the actin cytoskeletal remodelling required for leucocyte migration, inflammation and other biological functions [29]. Regarding inflammation, it was also interesting to find a striking increase in transcript levels for S100A8, S100A9 and S100A12 in SSc PBCs (Fig. 2). The S100/calgranulins are a group of pro-inflammatory calcium-binding proteins made and secreted by phagocytes [36]. S100A12, in particular, is involved in a novel inflammatory signal amplification pathway through interaction with the receptor for advanced glycation end products (RAGE) [37].

The first array studies of PBMCs in SLE patients reported increased expression of multiple type I and type II IFN-inducible genes [24, 25]. It is interesting to note that, of the 14 ‘IFN signature’ genes described by Baechler et al. in SLE PBMCs [24], six were also found to be differentially regulated in SSc PBCs, including G1P3, G1P2, MX1, RNASE2, PLSCR1 and SERPING1. Recently it has been reported that mononuclear cells from SLE patients had coordinated overexpression of a number of type I IFN genes and that these gene signatures correlated with disease activity [38]. In our study, the observed induction of IRF7 is of interest since this is an important regulator of the type I IFN response [39]. However, at this point it would be premature to conclude that there is a dominant type I IFN gene signature in SSc, since IRF7 can be activated by IFN-independent pathways [40]. Nonetheless, the parallels with the IFN signature in SLE remain striking.

The fact that SSc patients demonstrate activation of T cells is well described in the literature [9]. From this standpoint, it was interesting to find significantly increased expression of CFLAR in SSc PBCs (2-fold, P = 0.0007), since constitutive overexpression of CFLAR has been shown to drive the polarization of T-cell responses towards TH2 [41]. Conversely, we found that transcripts for CTLA4 were significantly reduced (2-fold, P = 0.0001) in SSc PBCs. CTLA4 is a costimulatory molecule expressed by CD4+ and CD8+ T cells that terminates the T-cell response by inhibiting activation signals delivered by CD28 [42]. Along the same lines, we observed significant differential regulation of multiple genes in the IL2RB pathway and the GATA3 pathway (Table 3). Both intermediate- and high-affinity isoforms of the IL-2 receptor contain a ß subunit; these isoforms are expressed in activated T cells, and are involved receptor-mediated endocytosis and transduction of mitogenic signals from IL-2 [43]. GATA3 is an important transcription factor in the regulation of human TH2 cell differentiation in vivo [44]. Thus, differential regulation of these pathways is consistent with the observed T-cell activation in SSc.

Although limited to the transcript level, the large amount of data obtained from gene expression studies provides a framework for future investigations into the complex cellular and genetic interactions that lead to SSc. From the point of view of understanding disease pathogenesis, it will be important to dissect out which cell types are responsible for the observed gene signatures in future studies. Such cell purification studies will need to systematically take into account and control for the changes in gene expression induced by the process of storage and purification of living cells [15, 16].

Another important question to be addressed in future studies is whether these IFN and vasculotrophic gene signatures are unique to SSc. Although comparison with published data on SLE reveal some parallels, a formal study using the same array platform with various autoimmune connective tissue diseases (e.g. SLE and RA) would be needed. From the standpoint of disease biomarkers, it would be important to determine whether these gene expression signatures could identify different clinical subsets of SSc or correlate with disease outcome or prognosis. We are unable to detect any differences in the SSc patients in or subanalysis. This is probably due to the loss of statistical power as a result of sample partitioning and because we are now assessing for subtle differences in subjects within the SSc group, which generally are more phenotypically similar to each other than are healthy controls. With a sufficiently large cohort of SSc patients, it is likely that such subtle differences would be detectable. All of these future studies will require careful case ascertainment and matching with healthy controls as well as disease controls to minimize confounding variables, such as age, gender and pharmacological interventions. Correlation with disease variables will require serial PBC samples and assessments of SSc disease activity in a sufficiently large longitudinal cohort. Whole-blood RNA stabilization makes undertaking these studies feasible, especially if multiple academic centres are involved. Such studies will require considerable effort and resources, but hold promise for the identification of candidate biomarkers for SSc disease activity or severity in the future.
Figure 3


   Acknowledgments
 
This work was supported by grants NIH-NCRR 3M01RR02558-12S1, NIH-NIAMS IP50AR4488, NIH-NIAMS N01-AR-02251 and NIH 5T35 DK007676, and grants from the Scleroderma Foundation.

The authors have declared no conflicts of interest.


   References
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 Introduction
 Methods
 Results
 Discussion
 References
 

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