Original Article | Published:

Peripheral blood gene expression profiling in Sjögren's syndrome

Genes and Immunity volume 10, pages 285296 (2009) | Download Citation



Sjögren's syndrome (SS) is a common chronic autoimmune disease characterized by lymphocytic infiltration of exocrine glands. The affected cases commonly present with oral and ocular dryness, which is thought to be the result of inflammatory cell-mediated gland dysfunction. To identify important molecular pathways involved in SS, we used high-density microarrays to define global gene expression profiles in the peripheral blood. We first analyzed 21 SS cases and 23 controls, and identified a prominent pattern of overexpressed genes that are inducible by interferons (IFNs). These results were confirmed by evaluation of a second independent data set of 17 SS cases and 22 controls. Additional inflammatory and immune-related pathways with altered expression patterns in SS cases included B- and T-cell receptor, insulin-like growth factor-1, granulocyte macrophage-colony stimulating factor, peroxisome proliferator-activated receptor-α/retinoid X receptor-α and PI3/AKT signaling. Exploration of these data for relationships to clinical features of disease showed that expression levels for most interferon-inducible genes were positively correlated with titers of anti-Ro/SSA (P<0.001) and anti-La/SSB (P<0.001) autoantibodies. Diagnostic and therapeutic approaches targeting interferon-signaling pathway may prove most effective in the subset of SS cases that produce anti-Ro/SSA and anti-La/SSB autoantibodies. Our results strongly support innate and adaptive immune processes in the pathogenesis of SS, and provide numerous candidate disease markers for further study.


Lymphocytic infiltration into exocrine glands is a hallmark of Sjögren's syndrome (SS) pathogenesis. Disruption of target organ function, particularly salivary and lacrimal gland secretion, may lead to severe and irreversible damage. The extent to which the exocrinopathy affects saliva and tear production varies, but moisture can be virtually non-existent and lead to corneal scarring, blurred vision, rampant dental caries, recurrent oral infections, and difficulty in speaking, swallowing and eating.1, 2 Extraglandular manifestations in SS are also common, heterogeneous and may involve the skin and genitourinary tracts, as well as the hematologic, neurologic, respiratory, gastrointestinal, vascular and musculoskeletal systems. Approximately half of SS cases experience lymphocytic-mediated organ damage.3 Increased risk of lymphoma in SS cases has been established, with estimates as high as 44-fold.4 About 50% of SS cases present an accompanying autoimmune rheumatic disease, most commonly, rheumatoid arthritis, systemic lupus erythematosus (SLE) or scleroderma.5

The molecular basis of SS is not well defined, but includes production of autoantibodies, dysfunction of molecular water transport processes, dysregulation of apoptosis and cytokine activity abnormalities.4, 6, 7, 8 A role for viral infection in SS has long been suspected but difficult to establish. Numerous viruses have been considered, including Epstein–Barr virus, cytomegalovirus, hepatitis C virus, human herpes virus 6, coxsackie virus and several retroviruses.9, 10 Specific evidence supporting these candidate viruses varies, but includes such properties as the ability to directly infect cells in the salivary gland and/or immune system, sequence similarities between viral proteins and autoantigens (particularly La/SSB), suggesting molecular mimicry, elevation of viral antibodies or viral sequences, association between viral infection and lymphoma, and association of symptoms mimicking SS after viral infection. Regardless of the specific virus, mechanisms of host–virus relationships that control or perpetuate latency/re-activation cycles of viral replication and inflammatory responses, such as production of interferons (IFNs), are likely to be important in SS.

Multiple genes are thought to increase disease susceptibility to SS, including human leukocyte antigen loci, interleukin 10 (IL-10), Fas, Fas ligand, and more recently, interferon regulatory factor 5 (IRF5).11, 12 Other polymorphisms have been found to be associated with various clinical features of SS. For example, association of anti-Ro/SSA autoantibody with the 52kD Ro/SSA gene,13 IgKM and GM genes with clinical presentation of SS,14 and apoE with early onset of SS have been described.15

Developments in high-throughput transcriptional profiling using microarray technology have dramatically expanded our ability to identify key molecular pathways related to the disease. Earlier studies using microarray approaches in SS have been limited to studies of salivary tissue in relatively small cohorts of cases. These studies have identified overexpression of interferon-inducible genes in salivary gland tissue from SS cases,16, 17 similar to that seen in other autoimmune diseases.18

The identification of biomarkers for SS in the peripheral blood mononuclear cells (PBMCs) or whole blood cells offers a very practical alternative to the current approaches for diagnosis and classification of SS cases.19 Furthermore, peripheral blood has proven to be informative for advancing our understanding of related autoimmune diseases, including SLE, rheumatoid arthritis, psoriasis and multiple sclerosis.18 In this study, we sought to identify important disease-associated pathways and explore correlations of gene expression profiles to relevant clinical features of SS.


Identification of an interferon-inducible gene signature in the peripheral blood mononuclear cells of SS cases

As an initial discovery effort, global mRNA transcript levels were measured in PBMCs of 21 SS cases and 23 healthy controls (Cohort 1) using Affymetrix U95A2 GeneChips containing 12 626 oligonucleotide probe sets. Demographic features of the participant cohorts are shown in Table 1. To identify differentially expressed transcripts between SS cases and controls, we used three data-filtering criteria: Welch's t-test P-value 0.001, mean fold change 1.5 and mean expression difference 100. A total of 425 mRNA transcripts representing 382 unique genes were identified as differentially expressed in SS cases (Figure 1a, Supplementary Table 1). Approximately 40 genes were identified more than once by multiple probe sets on the Affymetrix arrays. Significance levels for some transcripts reached P-values <10–14 and fold changes as high as 24 (Table 3, Supplementary Table 1). We observed 129 overexpressed and 296 underexpressed mRNA transcripts in SS cases relative to controls.

Table 1: Demographic and clinical data for SS cases
Figure 1
Figure 1

Gene expression profiles in two independent Sjögren's syndrome (SS) cohorts. Each row represents a single transcript and each column represents a single participant. SS cases (blue) and controls (orange) are indicated along the top of each cluster diagram and above each column of the expression data. Horizontal bars on the right of each diagram indicate interferon-inducible genes (purple), earlier defined by direct in vitro stimulation experiments or other data from the literature.22 Log2 transformed ratios of individual expression values divided by the mean of the controls were calculated for each transcript. These values were used in hierarchical clustering analyses. Relative intensities are indicated for overexpressed (red) and underexpressed (green) transcripts. (a) Differentially expressed transcripts (n=425) for Cohort 1. (b) Differentially expressed transcripts (n=120) for Cohort 2.

Unsupervised hierarchical cluster analysis was conducted to visualize patterns of the 425 differentially expressed transcripts (Figure 1a). Of the 129 overexpressed transcripts, 46% (n=59) are known to be inducible by IFNs (Figure 1a, Supplementary Table 1). Genes in this cluster include interferon-induced protein 35 (P=1.34 × 10–11), myxovirus (influenza virus) resistance 1 (P=9.94 × 10–8), 2′,5′-oligoadenylate synthetase 1 (P=1.05 × 10–7), IRF7 (P=1.98 × 10–7) and OAS2 (P=3.15 × 10–7).

We then used Ingenuity Pathways Analysis (IPA) software (version 5.5) to facilitate the systematic identification and grouping of differentially expressed genes into biological networks. Fifty-nine functional categories were identified by IPA as statistically significant for the 425 differentially expressed transcripts. Table 2 presents the top 20 most significant biological function categories (see Supplementary Table 2 for a list of all functional categories and sub-categories). Cell death was the most significant biological function with sub-category P-values ranging from 2.55 × 10–11 to 2.96 × 10−3, followed by cellular growth and proliferation (P=3.67 × 10–8–1.72 × 10–3) and immune and lymphatic system development and function (P=2.39 × 10–9–2.83 × 10–3).

Table 2: Top 20 most significant biological function categories identified through IPA

Ingenuity Pathways Analysis also identified 42 statistically significant canonical pathways from our list of differentially expressed transcripts in Cohort 1 (Supplementary Table 1). As shown in Figure 2, IFN signaling was the most significant pathway (P=1.57 × 10–5) followed by B-cell receptor signaling, insulin-like growth factor-1 (IGF-1) signaling, granulocyte macrophage-colony stimulating factor (GM-CSF) signaling, peroxisome proliferator-activated receptor (PPAR) signaling, PPARα/retinoid X receptor-α (RXRα) activation, T-cell receptor signaling, PI3/AKT (phophatidylinosital 3-kinase) signaling, acute-phase response signaling and JAK/STAT (janus kinase/signal transducer and activator) signaling among others (Figure 2). In general, transcripts involved in IFN signaling and protein ubiquitination were largely overexpressed, whereas the majority of transcripts from other pathways identified were underexpressed in SS cases versus controls. Significant overlap of differentially expressed genes was apparent across the 42 canonical pathways. For example, five genes (RRAS, KRAS, PIK3CA, PIK3R1 and PIK3CG) are multifunctional transcription factors or signaling molecules involved in over 20 of the 42 canonical pathways we identified. In addition, over 57% of the genes shown in Figure 2 mapped to the top nine most statistically significant pathways (P<0.001) identified by IPA. Within these nine, two sets of pathways were closely related: PPARα/RXRα activation/signaling and B-cell/T-cell receptor pathways. Of the remaining 33 pathways, 15 consisted entirely of genes that directly overlap with other pathways in Figure 2.

Figure 2
Figure 2

Summary of statistically significant canonical pathways identified through Ingenuity Pathways Analysis. Canonical pathways are listed across the top from left to right in the order of statistical significance in Cohort 1 with the P-value ranges indicated. Pathways indicated in bold italics represent those showing significance in both Cohorts 1 and 2. The left most column lists differentially expressed genes initially grouped by structural category to show cellular localization (extracellular, plasma membrane, cytoplasm or nucleus). The genes within each of the four structural categories are further organized by ranking each gene according to initial occurrence in the most significant canonical pathway as statistically ranked across the top from left to right. The color-coded boxes indicate the fold change differences in mean expression levels for Sjögren's syndrome cases in Cohort 1 relative to controls.

Replication of the interferon-inducible gene signature in the whole blood of SS cases

We next evaluated an independent group of 17 cases and 22 controls (Cohort 2, Table 1). Affymetrix U133A GeneChips with an expanded representation of 22 283 oligonucleotide probe sets were used to measure RNA transcript levels in this independent cohort. In addition to expanding the overall number of transcripts assayed in Cohort 2, we were also able to utilize more recently developed blood collection procedures that stabilize RNA transcript levels at the time of phlebotomy (see Materials and methods). As opposed to selecting a few transcripts for validation studies of our results from Cohort 1 (commonly done by quantitative PCR), this comparison provided a much more comprehensive approach for confirmation of the differentially expressed pathways through replication in an independent set of cases and controls.

Using the same three-step data-filtering approach (Welch's t-test P-value 0.001, mean fold change 1.5 and mean expression difference 100), 120 RNA transcripts in 100 genes (18 underexpressed and 102 overexpressed) were identified as differentially expressed in cases relative to controls (Supplementary Table 3). Cluster and pathway analysis of significant transcripts were used to identify gene expression patterns (Figures 1b and 2). Similar to the results in Cohort 1, the prominent signature of overexpressed IFN-inducible genes was observed in Cohort 2 (Figure 1b). Comparison of differentially expressed transcript lists for Cohort 1 and Cohort 2 resulted in identification of a total of 38 genes common to both the cohorts; the majority (n=34, 89%) of which represented IFN-inducible transcripts (Table 3). Thus, these genes represent a reproducible ‘IFN signature’ identifiable in the peripheral blood of SS cases.

Table 3: Independent replication of differentially expressed genes

Table 4 provides the results for selected IFN and IFN pathway regulators in both Cohorts 1 and 2. In general, the majority of IFN genes encoding the IFNs themselves were not differentially expressed in the peripheral blood. In contrast, IRF7, a key transcription factor involved in downstream signaling events triggered through IFN or Toll-like receptors, was upregulated by over twofold in both the data sets (Cohort 1 P=5.57 × 10−6, Cohort 2 P=1.98 × 10–7). Approximately 66% of the transcripts in this group were differentially expressed by twofold or greater in at least one cohort.

Table 4: Transcripts encoding selected interferons (IFNs) and IFN pathway regulators

It is possible that the difference in age observed between the cases and controls in Cohort 1 (mean for cases=57, mean for controls=31) may have contributed to a larger number of differentially expressed transcripts identified in Cohort 1 (n=425 in Cohort 1 and n=120 in Cohort 2). However, we believe that the use of whole blood in Cohort 2 is likely to have a greater effect in our ability to detect differential expression, as an excess of globin transcripts in the whole blood microarray experiments has been shown to mask signatures of biological relevance and produce fewer significant results when compared directly with PBMCs. Moreover, a direct comparison of our list of differentially expressed genes observed in Cohort 1 with a list of genes related to aging provided by the GenAge Database (http://genomics.senescence.info/genes/) resulted in very few overlaps (n=15). Additional evidence to support the association with SS for all of these 15 genes exists, either through results of other undergoing microarray studies (Moser KL, unpublished data), inclusion in significant biological pathways with several other genes identified as differentially expressed in this study, or earlier reports in the literature. Finally, despite the difference in number of differentially expressed transcripts between Cohorts 1 and 2, many of the significant canonical pathways were observed in both the cohorts. Using IPA, 10 statistically significant canonical pathways were identified in Cohort 2, nine of which were also observed in Cohort 1 (Figure 2, indicated in bold). One additional pathway, antigen presentation, was statistically significant in Cohort 2 (P=0.0025). In Cohort 1, the antigen presentation pathway was ranked 43rd and fell just below the threshold for significance (P=0.056).

Thus, using two independent cohorts, alternative versions of microarray GeneChips, and varying sample compositions of either PBMCs (Cohort 1) or whole blood cells (Cohort 2), we have observed consistent, reproducible overexpression of IFN-inducible gene expression patterns and identified several additional pathways characterized by downregulated patterns of gene expression in SS cases compared with the normal controls.

Correlation of interferon-induced gene expression patterns and key clinical features

We next wanted to explore the association between dysregulated pathways and clinical measures of SS. To maximize our statistical power, we generated a third, larger data set consisting of a total of 36 SS cases and 22 controls (Cohort 3). All data for Cohort 3 was generated from the whole blood using the Affymetrix U133A GeneChips containing 22 283 probe sets. We combined all available data from Cohort 2 with new data from Cohort 1 participants who were resampled using PAXgene tubes (and thus, assayed from the whole blood using the U133A GeneChip to be amenable for combining with Cohort 1 data). As Cohort 3 included all the participants from both Cohort 2 and most participants from Cohort 1, analysis of this third data set was not considered independent from the results described above, but did allow more statistically robust results given the larger sample size for correlation analyses (see Materials and methods). The clinical variables evaluated included saliva production measured by whole unstimulated salivary flow (WUSF), tear flow measured by Schirmer's test (ST) and titers of anti-Ro/SSA and anti-La/SSB autoantibodies determined by enzyme-linked immunosorbent assay.

Figure 3a shows the hierarchical cluster graph of 223 RNA transcripts in 193 genes (197 overexpressed and 86 underexpressed) identified as differentially expressed between Cohort 3 SS cases and controls, using the three-filter criteria (Supplementary Table 4). The distributions of clinical variable values are shown in Figure 3b. Correlation coefficients for RNA expression values with tear flow, salivary flow and autoantibody titers (measured in the same sample for each individual) were estimated for the 223 differentially expressed RNA transcripts in the group of 36 SS cases. Healthy controls were not included in these analyses so that we could assess significant correlations defined within the case group only.

Figure 3
Figure 3

Correlation of clinical features with gene expression profiles in Sjögren's syndrome (SS). (a) Hierarchical clustering analysis of 223 differentially expressed transcripts between 36 SS cases (blue) and 22 controls (orange) in Cohort 3. Color coding is as described in Figure 1. (b) Bar graphs showing the distribution of measurements for anti-Ro/SSA (blue) and anti-La/SSB (gold) autoantibodies as measured by enzyme-linked immunosorbent assays, tear flow measurements as measured by Schirmer's tests (maroon) and whole unstimulated salivary flow (green) for each individual in panel a. (c) Correlations between RNA transcript levels (rows) in panel a and clinical measurements of autoantibodies, tear flow and salivary flow. Dashed lines indicate statistical significance thresholds (P=0.05) determined through permutation testing.

As shown in Figure 3c, most of the correlation tests did not reach statistical significance (P>0.05) for salivary flow or tear flow (WUSF and ST, respectively). This is an expected result because all SS cases are ascertained on the basis of the reduced values for these clinical variables. Of the 223 RNA transcripts, only 11 were significantly correlated with salivary flow (5%) and 17 with tear flow (8%). Of the 86 underexpressed RNA transcripts, 6% correlated with titers of anti-Ro/SSA and anti-La/SSB autoantibodies (3 and 5 transcripts, respectively). In contrast, a large proportion of the 197 overexpressed RNA transcripts were positively correlated (P<0.05) with titers of anti-Ro/SSA (n=89 or 45% of the transcripts) and anti-La/SSB (n=76 or 39% of the transcripts). Approximately two-thirds of the RNA transcripts that were correlated with anti-Ro/SSA and/or anti-La/SSB autoantibodies are known to be IFN-inducible genes. Correlations between the clinical variables tested and transcripts involved in other dysregulated pathways identified in Cohorts 1 and 2 (for example, B-/T-cell receptor signaling, IGF1 receptor (R), GM-CSF signaling and so on) were not observed (Figure 3).


We have applied microarray technology to define global gene expression profiles in SS, and identified several key pathways that are dysregulated in cases versus normal controls. Our study is the first to show that upregulation of IFN-inducible gene expression is prominent in the peripheral blood cells of many SS cases, and correlates with high titers of anti-Ro/SSA and anti-La/SSB. In addition, analysis of two independent cohorts showed evidence for dysregulation of signaling through the B-cell/T-cell receptors, IGF-1, GM-CSF, PPARα/RXRα, and several cytokine pathways that seem to be consistent across all SS cases.

Microarray-based studies in human pSS have earlier focused on the identification of disease-associated pathways in saliva or in minor salivary gland tissue from relatively small cohorts (10 or fewer cases plus controls).16, 17, 20, 21 A common finding across the four studies reported to date is upregulation of IFN-inducible genes. Genes overexpressed in our data generated using the peripheral blood that have also been reported as upregulated in minor salivary glands and/or saliva from SS cases include, interferon-induced transmembrane proteins 1 (9–27 U, 9–27, IFITM1) and 3 (1–8U, IFITM3), promyelocytic leukemia, transporter 2 ATP-binding cassette, spleen tyrosine kinase, guanylate binding protein, 2 and interferon-induced protein 44.16, 17, 20 These genes and others that show similar consistency across multiple sample types underscore both the local and systemic nature of IFN pathway dysregulation. Furthermore, these genes may serve as especially attractive targets for the development of clinically useful biomarkers. Disease markers that are both central to pathology in target tissues (for example, salivary glands) and potentially more feasible to assay through saliva or serum-based diagnostic tests would provide a significant improvement over the current approaches for the classification of SS cases.

In recent years, upregulation of IFN pathway signaling has been noted in a growing list of autoimmune disorders, including psoriasis, multiple sclerosis, rheumatoid arthritis, dermatomyositis, primary biliary cirrhosis and insulin-dependent diabetes mellitus.18 The IFN-inducible gene expression profile that we report in SS is remarkably similar to the ‘IFN signature’ that has been observed in similar studies of peripheral blood in SLE, present in a majority of cases22 (Moser KL, unpublished observations). In addition to overlap of certain clinical features in both SLE and SS, production of anti-Ro/SSA and anti-La/SSB autoantibodies are common in both disorders. In our study, the IFN signature in SS was significantly correlated with high titers of anti-Ro/SSA and anti-La/SSB. Although the precise underlying disease mechanism connecting IFN pathway activation and autoantibody production is unclear, these results provide further support to link both innate and adaptive immune responses to the pathogenesis of disease.

Activation and control of IFN-inducible genes may be dysregulated because of the abnormal levels or activity of a class of transcription factors known as IRFs. For example, IRF1 and IRF2 are structurally similar DNA-binding factors that were originally identified as regulators of the Type I IFN system; IRF1 functions as a transcriptional activator, and IRF2 represses IRF1 function by competing for the same cis elements.23 Evidence from our data sets suggests that IRF1 is upregulated and IRF2 is downregulated in SS cases. Such an imbalance is consistent with upregulation of IFN-inducible genes. Furthermore, IRF5 and IRF7, both upregulated in our data, play a crucial role in the expression of Type I IFN genes, cytokines and some chemokines.24, 25 It is interesting that, Epstein–Barr virus regulates and uses IRF7 as a secondary mediator for several target genes involved in latency and immune regulation. In addition, Ning et al.26 have shown that the virus-activated factor of Sendai virus binds to IRF7 IFN-stimulating element, and can directly activate IRF7 transcription independent of IFN-triggered JAK-STAT pathway. Finally, genetic association of polymorphisms in IRF5 and STAT4, directly involved in IFN pathway signaling, with both SLE and SS has been reported.12, 27, 28, 29, 30

Collectively, these observations indicate that overexpression of IFN-responding genes in SS may result not from overexpression of IFN genes themselves, but rather from effects mediated more directly by viral infection and/or genetic variants in IRFs and other IFN pathway mediators that contribute to altered signaling. The potential role of the Type I interferon system in SS was recently reviewed by Nordmark et al.31 Current data supports a mechanism of disease in which an initial viral infection induces Type I interferon production in salivary glands, leading to apoptosis or necrosis of glandular epithelial cells and exposure of autoantigens, such as anti-Ro/SSA and anti-La/SSB, followed by activation of adaptive immune responses (both locally and systemically). Production of autoantibodies (including anti-Ro/SSA and anti-La/SSB) that form immune complexes with nucleic acids may trigger prolonged activation of IFN pathways through Toll-like receptor-medicated stimulation of plamacytoid dendritic cells (pDCs).31 Additional production of IFNs, as well as cytokines known to be relevant to SS including, IL-12, IL-6, TNF, CXCL10 and CCL3, can be produced by pDCs, leading to recruitment and perpetuation of a continuous cycle if not properly downregulated.32 Consequently, this process leads to impaired function of affected exocrine glands and potential systemic manifestations commonly seen in SS patients. Our results showing correlations between IFN pathway activation and autoantibodies bring up important considerations for the development of improved diagnostic and therapeutic strategies. We propose that the development of biomarkers, which reflect the IFN signature and therapies directed against IFN pathway activation, are most likely to be successful in the subset of patients with high titers of anti-Ro/SSA and/or anti-La/SSB.

Ingenuity Pathways Analysis identified 59 functional categories associated with the list of differentially expressed genes identified in Cohort 1. We found these categories to be too broad for the development of hypotheses of disease mechanisms, and as a result, have focused our attention on canonical pathways. In addition to upregulation of an IFN-inducible gene expression pattern, we identified over 40 additional canonical pathways that were differentially expressed in our PBMC data set using IPA. However, these pathways do not seem to be independent of each other. Close examination of the genes included in these pathways showed a significant amount of overlap, most likely reflecting the extensive ‘crosstalk’ that occurs among closely related biological pathways. These results suggest that certain pathways, such as those initiated through B- or T-cell receptor signaling, account for the seemingly large number of the pathways identified by using approaches such as IPA.

Several of the canonical pathways and dysregulated genes (outside of the ‘IFN signature’) represent interesting and potentially important new avenues for further investigation. For example, B-cell/T-cell receptor signaling was significantly dysregulated in this study. One of the genes in these pathways encodes protein-tyrosine phosphatase receptor-type C (PTPRC) (also known as CD45, CD45R and Ly5), which is a major leukocyte cell surface molecule that suppresses JAK kinase and negatively regulates cytokine receptor signaling.33 PTPRC is essential for activation of T cells and B cells, and important for integrin-mediated adhesion and migration of immune cells. In our data, PTPRC was overexpressed in cases versus controls, consistent with enhanced downregulation of other B-/T-cell pathway genes observed. Targeted disruption of PTPRC has been shown to enhance cytokine and interferon receptor-mediated activation of JAK and STAT proteins.33 Furthermore, genetic associations of variants in PTPRC have been reported with multiple sclerosis, Grave's disease and Hashimoto's thyroiditis.34 In murine models, genetic variants in PTPRC lead to lymphoproliferation and severe autoimmune nephritis with autoantibody production and alterations in cytokine production. Thus, evaluation of PTPRC and other related members of lymphocyte signaling pathways may be informative in further defining autoimmune responses in SS.

The insulin-like growth factor-1 receptor was underexpressed in our study, consistent with a study of SS minor salivary glands by Katz et al.35 Low levels of IGF1R have also been shown in the non-obese diabetic mouse model of experimental autoimmune sialadenitis.36 Dysregulation of this pathway may result in the inability of IGF1 to exert its homeostatic, protective effect in salivary tissue and leads to glandular atrophy and disfunction.35

Altered signaling through PPARα/RXRα pathways also offers intriguing clues to SS pathogenesis. PPARs (peroxisome proliferator-activated receptors) are nuclear receptors, which when activated by ligand, form a functional transcriptional unit on heterodimerization with RXRs.37 PPARα and the related family members are critical modulators of environmental and dietary stimuli, and play a key role in downregulating inflammatory responses.37, 38 In immune cells, PPARα inhibits inflammatory pathways through sequestration and repression of c-jun and NF-κB transcription factors.38, 39 Underexpression of PPARα in SS cases relative to controls, as observed in our study, is thus consistent with a pro-inflammatory process. Interestingly, studies in experimental autoimmune encephalitis, a murine model of multiple sclerosis, have shown baseline lower expression levels of PPARα in CD4+ T cells from females relative to males, resulting in increased NF-κB and c-jun activity, higher production of IFN-γ and tumor necrosis factor and thus, differential regulation of PPARα between genders may contribute to increase risk of disease in females with multiple sclerosis and other autoimmune diseases.40 Agonists of PPARα have been proposed as a potential therapeutic approach in multiple sclerosis and several other autoimmune and inflammatory disorders associated with decreased PPARα expression, such as psoriasis and atopic dermatitis.41 Furthermore, PPARα agonists have been proposed as an effective therapeutic intervention for treatment of dry eye in SS.42 Thus, further studies should be considered to explore the potential application of PPARα agonists as novel therapeutic agents.

In summary, using varying peripheral blood cell populations (mononuclear cells and whole blood), two independently collected cohorts of cases and controls, and two different versions of Affymetrix GeneChips (U95A and U133A), we have shown a consistent upregulation of IFN-inducible genes in SS cases. Our results further show that this pattern is most prominent in the subset of cases serologically defined by increased titers of anti-Ro/SSA and anti-La/SSB autoantibodies. We also identified numerous additional signaling pathways that collectively support a significant role for both innate and adaptive immune dysregulation in SS. These results should foster multiple lines of further investigation, including genetic and functional studies, which will hopefully lead to new insights into pathogenesis of this complex autoimmune disorder.

Materials and methods

Case characteristics

All protocols used in this study were approved by the University of Minnesota Institutional Review Board. All participants provided written informed consent before entering the study. All SS cases met the 2002 Revised European Criteria proposed by the American European Consensus Group.19 Accordingly, cases were classified with SS if they had an autoimmune component (detection of anti-Ro/SSA and/or anti La/SSB autoantibodies) and/or evidence of lymphocytic infiltration through labial salivary gland histopathology, plus characteristic symptoms (dry eyes and dry mouth) and signs (decreased tear flow measured by Schirmer's test or decreased WUSF). Cohorts 1 and 2 consisted of independent participants. Cohort 3 included 19/21 cases from Cohort 1 (samples redrawn, see below) plus all 17 cases from Cohort 2. Two of the cases in Cohort 1 also met the ACR criteria for SLE.

The controls were asymptomatic for dry eyes and dry mouth, and had no self-reported family history of autoimmune diseases. The first group of controls (n=17) consisted of all female Caucasians with an average age of 31, which were used for comparison with case Cohort 1. The second group of controls (Cohort 2) was all female with 21/22 reporting Caucasian ancestry. These controls had a mean age of 51 and were used for analysis of both case Cohorts 2 and 3.

The data collection procedures consisted of the participant's interviews, completion of a detailed questionnaire, review of medical records, physical examination, Schirmer's test without anesthesia (5 min), unstimulated salivary flow measurement (15 min), and phlebotomy for RNA extraction and determination of anti-Ro/SSA and anti-La/SSB autoantibodies.

Sample preparation and hybridization

Total RNA was extracted from PBMCs by Trizol (GIBCO/BRL, Invitrogen, Carlsbad, CA, USA) or from whole blood using the PAXgene Blood RNA method (QIAGEN/BD, Valencia, CA, USA). The methods for preparation of complimentary RNA (cRNA) were provided by the manufacturer (Affymetrix, Santa Clara, CA, USA; GeneChip technical manual). Briefly, 5–10 mg of total RNA of each sample was converted into double-stranded complimentary DNA (cDNA), using a Superscript cDNA synthesis kit (Invitrogen) with an oligo(dT)24 primer. After second strand synthesis, labeled cRNA was generated from the cDNA sample by an in vitro transcription reaction using BioArray labeled biotin ribonucleotides (Enzo, New York, NY, USA). The labeled cRNA was purified using RNAeasy spin columns (Qiagen, Valencia, CA, USA). Fifteen micrograms of each cRNA sample was fragmented by mild alkaline treatment at 94 °C for 35 min in fragmentation buffer (Tris Acetate pH 8.1/1 M, 150 mM MgoAc and 500 mM KoAc). Fragmented cRNAs were hybridized to Affymetrix Human U95Av2 or U133A GeneChips.

All Cohort 1 samples were collected in CPT tubes and processed within 4 h of phlebotomy. However, given ex vivo changes that can be observed in expression levels for a substantial fraction of genes shortly after phlebotomy,43 whole blood was directly collected into PAXgene tubes for Cohorts 2 and 3, which contain an RNA stabilizing agent. As a result, blood sample composition for Cohorts 2 and 3 (whole blood) were different than for Cohort 1 (PBMCs). A total of 19 participants were drawn twice; first for inclusion in Cohort 1 and later for inclusion in Cohort 3.

Anti-Ro/SSA and anti-La/SSB autoantibody assays

The levels of anti-Ro/SSA and anti-La/SSB autoantibodies in the serum of SS cases and controls were measured by enzyme-linked immunosorbent assay (Immunovision, Springdale, AR, USA). Absorbance was measured at 490 nm. The cutoff absorbance value above which antibody levels were considered positive was set to the mean plus two times the s.d. of titer values for controls.

Data processing

Initial data processing involved several quality control checks assessing the starting and amplified RNA and the overall hybridization process. Quality control criteria included: (1) the ratio of 3′–5′ probe sets should be <3; (2) more than 30 percent of genes should be called ‘present’; and (3) the murine sequences received an ‘absent’ call, whereas human ‘housekeeping’ sequences received a ‘present’ call.

We used GeneData Expressionist database and software (http://www.genedata.com) for further processing and analyzing the data. The MAS 5.0 (Affymetrix Microarray Suite 5) algorithm was used for data normalization. Gene expression intensity for each array was scaled to 1500 intensity units to allow comparison across all arrays. The scaled expression intensities were imported into GeneData Analyst (version 4.2) for statistical analysis.

Gene selection for hierarchical cluster analysis

In all the three cohorts, transcripts were defined as differentially expressed and selected for cluster analysis if, for the mean comparison between SS cases and healthy controls, the following criteria were met: (1) P-value of 0.001 or less from Welch's t-tests; (2) change in mean expression of at least 1.5-fold; (3) mean expression difference of at least 100 units is 22. Hierarchical cluster analysis was applied to the three data sets using CLUSTER software and visualized using TREEVIEW software.44

Correlation of gene expression and clinical variables

The Pearson's correlation estimates and P-values between transcript levels and clinical variable measurements (anti-Ro/SSA, anti-La/SSB, WUSF and ST) were computed for each of the differentially expressed transcripts. P-values of correlations for each transcript were plotted as a moving window average across units of five transcripts.45

Identification of canonical pathways

Ingenuity Pathways Analysis (version 5.5) software (https://analysis.ingenuity.com) was used to determine significant functional categories and canonical pathways based on our lists of significant transcripts. IPA tests associations between specified genes and sets of functional genes that are part of biologically relevant networks according to the literature findings. Right-tailed Fisher's exact tests are used to measure the probability that such associations are because of chance. The proportion of genes mapped to a specific canonical pathway that are specified by the user is taken into account for the computation of P-values.

Conflict of interest

The authors declare no conflict of interest.


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This study was funded by NIH NIAMS RO1 AR050782 and the Phileona Foundation (KLM). The authors are grateful for resources provided by the University of Minnesota Supercomputing Institute and the Affymetrix core. We also thank Carolyn M Meyer, Amber N Leiran, Liliana Tobon, Daniella Machado and Julie Ermer for their technical assistance, and Jennifer Lessard for assistance with graphics. Finally, we thank the study participants without whom this study would not be possible.

Author information


  1. Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN, USA

    • E S Emamian
    •  & N L Rhodus
  2. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA

    • J M Leon
  3. Arthritis and Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA

    • J M Leon
    • , C J Lessard
    • , P M Gaffney
    •  & K L Moser
  4. Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA

    • C J Lessard
    •  & K L Moser
  5. Allina Medical Clinic, St Paul, MN, USA

    • M Grandits
  6. Department of Medicine, University of Minnesota, Minneapolis, MN, USA

    • E C Baechler
    •  & B Segal


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Correspondence to K L Moser.

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