Sialic acidbinding Ig-like lectin 1 expression in inflammatory and resident monocytes is a potential biomarker for monitoring disease activity and success of therapy in systemic lupus erythematosus.код для вставкиСкачать
ARTHRITIS & RHEUMATISM Vol. 58, No. 4, April 2008, pp 1136–1145 DOI 10.1002/art.23404 © 2008, American College of Rheumatology Sialic Acid–Binding Ig-like Lectin 1 Expression in Inflammatory and Resident Monocytes Is a Potential Biomarker for Monitoring Disease Activity and Success of Therapy in Systemic Lupus Erythematosus Robert Biesen,1 Cemal Demir,2 Fidan Barkhudarova,1 Joachim R. Grün,2 Marta Steinbrich-Zöllner,2 Marina Backhaus,1 Thomas Häupl,1 Martin Rudwaleit,1 Gabriela Riemekasten,1 Andreas Radbruch,3 Falk Hiepe,1 Gerd-Rüdiger Burmester,1 and Andreas Grützkau2 prominent type I IFN–regulated candidate genes. At the protein level, the frequency of Siglec-1–expressing monocyte subsets was correlated with disease activity (as measured by the SLE Disease Activity Index) and was inversely correlated with levels of complement factors. Most interestingly, levels of anti–doublestranded DNA (anti-dsDNA) antibodies were highly correlated with the percentage of resident monocytes, but not inflammatory monocytes, expressing Siglec-1. High-dose glucocorticoid treatment resulted in a dramatic reduction of Siglec-1 expression in cells from patients with active SLE. Conclusion. Our findings indicate that Siglec-1 expression in resident blood monocytes is a potential biomarker for monitoring disease activity, displaying type I IFN responses, and estimating levels of antidsDNA antibodies. Moreover, our results suggest that resident and inflammatory monocytes contribute differently to the process of autoantibody formation in SLE. Objective. Type I interferon (IFN) plays a pivotal role in the pathogenesis of systemic lupus erythematosus (SLE) and is therefore considered a potential therapeutic target. This study was undertaken to establish a feasible biomarker for IFN effects with respect to disease activity and effectiveness of IFN-suppressive therapy in SLE patients. Methods. Transcriptomes of purified monocytes from 9 SLE patients and 7 healthy controls were analyzed by Affymetrix GeneChip technology. Levels of sialic acid–binding Ig-like lectin 1 (Siglec-1) (sialoadhesin, CD169) in inflammatory and resident monocytes were determined at the protein level in 38 healthy controls and 52 SLE patients, using multicolor flow cytometry. Results. Transcriptomes of peripheral monocytes from SLE patients revealed a dominant type I IFN signature. Siglec-1 was identified as one of the most Supported by the German Federal Ministry of Education and Research through the National Genome Research Network (Infection & Inflammation Network, SIPAGE) and the European Union (AUTOROME, LSHM-CT-2004-005264, AutoCure, LSHB-CT-2006018661, and EURO-RA, MRTN-CT-2004-005693). 1 Robert Biesen, MD, Fidan Barkhudarova, Marina Backhaus, MD, Thomas Häupl, MD, Martin Rudwaleit, MD, Gabriela Riemekasten, MD, Falk Hiepe, MD, Gerd-Rüdiger Burmester, MD: Charité Universitätsmedizin Berlin, Humboldt University of Berlin and Free University, Berlin, Germany; 2Cemal Demir, Joachim R. Grün, PhD, Marta Steinbrich-Zöllner, Andreas Grützkau, PhD: German Arthritis Research Center (DRFZ), Berlin, Germany; 3Andreas Radbruch, PhD: Charité Universitätsmedizin Berlin, Humboldt University of Berlin and Free University, Berlin, and German Arthritis Research Center (DRFZ), Berlin, Germany. Address correspondence and reprint requests to Andreas Grützkau, PhD, German Arthritis Research Center (DRFZ), Charitéplatz 1, D-10117 Berlin, Germany. E-mail: Gruetzkau@drfz.de. Submitted for publication August 1, 2007; accepted in revised form December 14, 2007. Systemic lupus erythematosus (SLE) is a chronic relapsing inflammatory autoimmune disease that often affects women of childbearing age. The disease is characterized by alterations in both the innate and the adaptive immune systems. Many of these alterations are known to be effects of type I interferon (IFN), a stress cytokine in the immune systems. IFN has been suspected in the pathogenesis of SLE for 20 years, but its pivotal role has been clarified only recently (1–3). An IFN signature is detectable on different biologic levels: in blood cells, in sera, in inflamed joints, and even in end organs such as the kidney (4–7). Expression of different IFN-inducible genes correlates with disease activity and severity (8–10), while serum levels of IFN␣ correlate 1136 SIGLEC-1 AS A TYPE 1 INTERFERON SURROGATE MARKER IN SLE with titers of double-stranded DNA (dsDNA) autoantibodies (11,12). Several groups have described IFN signatures in peripheral blood mononuclear cells (PBMCs), but their origin and the contribution of defined cell types remain unclear (4,10,13,14). These limitations prompted us to identify cell-specific IFN signatures in whole blood from patients with SLE. Two subpopulations of monocytes, inflammatory monocytes and resident monocytes, circulate in the bloodstream. Inflammatory monocytes are CD14⫹⫹, CD16⫺, and CD32⫹⫹. They are key players in the first line of defense against infectious agents (15). Resident monocytes are CD14⫹,CD16⫹ and resemble mature tissue macrophages in phenotype and function. The contribution of specific subsets to the pathogenesis of SLE is unclear. Information regarding these contributions may lead to a better understanding of disease mechanisms. In this study, we focused on monocytes because their pathophysiologic contribution to SLE is apparent on at least 3 mechanistic levels. First, increased apoptosis of monocytes leads to generation of blood nucleosomes, which are a major target of the autoantibody immune response in SLE (16,17). Moreover, this accelerated apoptosis of monocytes is associated with the formation of autoantibodies and with organ damage (18,19). Second, a clearance deficiency of monocytes and macrophages causes accumulation of apoptotic bodies in different tissue types (20,21). Third, some monocytes become professional antigen-presenting cells upon stimulation with IFN␣ and may present processed antigens of ingested apoptotic cells and nucleosomes to CD4⫹ T cells that subsequently become activated (22). Growing knowledge about the pivotal role of the IFN system in SLE leads to implications for therapeutic intervention. Currently, only high-dose glucocorticoids have been proven to suppress the effects of IFN (10). Interestingly, hydroxychloroquine, which is often used as a first-line treatment, is also believed to act via IFN suppression through stabilization of microsomes and interference with Toll-like receptors (23). Plasmapheresis probably also reduces levels of IFN by removing IFN-inducing circulating immune complexes (24). Moreover, new approaches targeting type I IFN–like antibodies against IFN, soluble IFN receptors, monoclonal antibodies (mAb) against blood dendritic cell antigen 2, and others are under development (3,25). In transcriptome analysis of SLE monocytes, we found a tightly clustering IFN signature including a transcript encoding for the surface protein sialic acid– binding Ig-like lectin 1 (Siglec-1) (sialoadhesin, CD169), which was highly up-regulated. Siglec-1 is a macrophage- 1137 restricted receptor that can bind to granulocytes, erythrocytes, and B cells and to CD43 on T cells (26,27). Siglec-1–positive monocytes have previously been found in SLE patients with renal involvement, in patients with human immunodeficiency virus (HIV) infection, and in patients with systemic sclerosis (SSc), implicating the involvement of type I IFN in these diseases (28–30). In this study, we validated the disease-dependent expression of Siglec-1 in the peripheral blood of SLE patients at the protein level by multicolor flow cytometry. The value of Siglec-1 as a biomarker for type I IFN–driven pathologic mechanisms was estimated with respect to its correlation with various clinical parameters. Since targeting of IFN is a promising new therapeutic option, we also analyzed whether Siglec-1 is able to reflect suppression of IFN effects. PATIENTS AND METHODS Study participants. For transcriptome analysis, a total of 9 patients with SLE, 10 patients with SSc, 8 patients with rheumatoid arthritis (RA), 12 patients with ankylosing spondylitis (AS), 4 patients with osteoarthritis (OA), and 7 healthy donors were studied. Replicate analyses were performed for 2 of the donors, giving a total of 9 healthy samples. Of the 9 patients with SLE, 7 were white and 2 were Asian. To be included in the study, SLE patients had to fulfill at least 4 of the 11 components of the American College of Rheumatology (ACR) criteria (31). All SLE patients had active disease, and their mean score on the SLE Disease Activity Index (SLEDAI) (32) was 17 (range 7–26). SLE patients had a mean C-reactive protein (CRP) level of 5.7 mg/liter, and a mean erythrocyte sedimentation rate (ESR) of 40 mm/hour. Patient sera were positive for anti-dsDNA autoantibodies, and renal involvement was evident. All SLE patients were treated with prednisolone (5–25 mg/day). In addition, 4 patients received intravenous cyclophosphamide (800 mg/month), 2 patients received azathioprine, and 1 received antimalarials. Of the SSc patients (n ⫽ 10), 6 were classified as having limited scleroderma, and 4 were classified as having diffuse scleroderma, according to the ACR (formerly, the American Rheumatism Association) criteria (33). Modified Rodnan skin thickness scores (34) ranged from 5 to 34, and 3 patients with diffuse scleroderma had evident fibrosis of the lung. Four patients were treated with prednisolone (5–7.5 mg/day), 2 received intravenous cyclophosphamide (800 mg/ month), 1 received antimalarials, and 1 received methotrexate (MTX; 15 mg/week). RA patients (n ⫽ 8) were diagnosed according to the ACR criteria (35). All patients presented with active disease, defined by a mean ⫾ SD Disease Activity Score in 28 joints (36) of 6.1 ⫾ 1.1. The mean ⫾ SD CRP level was 50.3 ⫾ 53.5 mg/liter. Rheumatoid factor was positive in 4 patients (mean ⫾ SD of 210 ⫾ 198 IU/ml), and 2 patients were HLA–DR4 positive. Five patients were treated with nonsteroidal antiinflammatory drugs (NSAIDs) only, 1 received MTX (15 mg/ week) combined with NSAIDs, 1 received a combination of 1138 BIESEN ET AL Table 1. Characteristics of the SLE patients and normal donors* SLE patients Normal donors (n ⫽ 52) (n ⫽ 38) Age, mean (range) years 38 (19–71) Sex, no. (%) female 47 (90.4) Ethnicity White, no. (%) 48 (92.3) Asian, no. (%) 4 (7.7) No. of ACR criteria fulfilled, mean 5.1 (4–8) (range) Disease duration, mean (range) years 6.76 (0–40) ESR, mean (range) mm/hour 51.5 (5–115) 38 (18–61) 35 (92.1) 38 (100) 0 (0) NA NA NA * SLE ⫽ systemic lupus erythematosus; ACR ⫽ American College of Rheumatology; NA ⫽ not applicable; ESR ⫽ erythrocyte sedimentation rate. MTX (15 mg/week) and leflunomide (10 mg/day), and 1 received prednisolone (15 mg/day) combined with sulfasalazine (2 gm/day). All AS patients (n ⫽ 12) had clinically active disease, defined by a mean ⫾ SD Bath Ankylosing Spondylitis Disease Activity Index (37) score of 6.0 ⫾ 1.5. In all patients, functional status was impaired (mean ⫾ SD Bath Ankylosing Spondylitis Functional Index  score 5.3 ⫾ 1.6), and mobility was restricted (mean ⫾ SD Bath Ankylosing Spondylitis Metrology Index  score 3.1 ⫾ 3.1). All AS patients were HLA–B27 positive. Patients were treated with NSAIDs but were not receiving disease-modifying antirheumatic drugs or immunosuppressants. OA patients (n ⫽ 4) were diagnosed based on findings of clinical evaluations, excluding metabolic causes. All patients were awaiting knee joint replacement surgery. The group of normal donors (n ⫽ 7) included healthy subjects ages 20–60, who were not receiving any medications and had no indications of inflammation (ESR ⬍30 mm/hour and CRP level ⬍5 mg/liter). The characteristics of the 52 SLE patients and 38 normal donors who were analyzed for independent validation of Siglec-1 are shown in Table 1. The Ethics Committee of the Medical Faculty of Charité Universitätsmedizin Berlin approved the study, and written informed consent was obtained from all subjects. Blood collection and flow cytometry for Siglec-1 validation. Leukocytes in 4 ml of blood were separated by lysing erythrocytes at 4°C with EL buffer (Qiagen, Hilden, Germany) and washing cells twice with 50 ml PBS containing 5 mM EDTA (Sigma, Munich, Germany). Anti–Siglec-1 (CD169) clone HSn 7D2 (Abcam, Cambridge, UK) was used for staining. Fluorescein isothiocyanate (FITC)–conjugated goat F(ab⬘)2 anti-mouse IgG was used as the secondary antibody (Dianova, Hamburg, Germany), and mouse immunoglobulins (Dianova) were used to block remaining binding sites of the secondary antibody before adding phycoerythrin (PE)– conjugated anti-CD14 (Becton Dickinson, Heidelberg, Germany) and allophycocyanin (APC)–Cy7–conjugated antiCD16 antibodies (Becton Dickinson). The same staining procedure without the primary mAb was used as a negative control. Samples were analyzed on an LSR II cytometer (Becton Dickinson), and frequencies of CD169⫹ resident monocytes and CD169⫹ inflammatory monocytes were calculated after appropriate gating. Blood collection and cell separation for gene chip analysis. Peripheral blood (50 ml) was collected in Vacutainer tubes containing heparin (Becton Dickinson) and processed within 120 minutes. Briefly, blood was fractionated simultaneously in 4 major leukocyte populations (monocytes, CD4⫹ and CD8⫹lymphocytes, and natural killer cells) by a combination of erythrocyte lysis, magnetic-activated cell sorting (MACS; Miltenyi Biotec, Bergisch Gladbach, Germany), and high-speed fluorescence-activated cell sorting (FACS). This procedure was optimized in order to obtain cell populations with purities and viabilities of ⱖ95%. Temperature- and Ficoll-induced transcriptional alterations were reduced as much as possible by maintaining a temperature of 4°C and by avoiding any density gradient medium for the preenrichment of PBMCs (40). Erythrocytes were lysed at 4°C with EL buffer, according to the recommendations of the manufacturer (Qiagen). After lysis was completed, leukocytes were washed twice with 50 ml phosphate buffered saline (PBS)–bovine serum albumin containing 5 mM EDTA. Leukocytes were quantified with a CASY cell counter (Schärfe System, Reutlingen, Germany) and incubated with an appropriate amount of CD15conjugated microbeads (MACS; Miltenyi Biotec). Separation was performed using an automated separation system (autoMACS; Miltenyi Biotec) using the separation program “possel_s.” The fraction of CD15⫺ cells was counted and prepared for 4-channel high-speed FACS (FACSVantage SE equipped with a FACSDiVa option; Becton Dickinson). Cells were stained for 15 minutes at 10°C with an antibody cocktail containing CD3-APC (Becton Dickinson), CD56-PE (Miltenyi Biotec), CD14-FITC (Becton Dickinson), CD4–PE-Cy5 (Caltag, Hamburg, Germany) and CD8–PE-Cy7 (Caltag) mAb. After washing, cells were filtered using 30-m nylon mesh filters (Miltenyi Biotec), and 4⬘,6-diamidino-2phenylindole was added to identify dead cells during FACS analysis. Stained cells were sorted under continuous cooling at a sorting rate of 25,000 events/second and an operating system pressure of 35 psi. After sorting, cell populations showed purities of ⱖ97% and cell viabilities of ⱖ99%. Sorted cells were immediately lysed in buffer RLT containing 1% ␤-mercaptoethanol, and lysates were stored at ⫺80°C until RNA isolation. RNA isolation and Affymetrix GeneChip hybridization. Total RNA was extracted using the RNeasy Mini kit (Qiagen). Contaminating genomic DNA was removed by an on-column DNA digestion step (Qiagen). The amount and integrity of RNA isolated were assessed for each sample using an Agilent 2100 Bioanalyzer (Agilent, Waldbronn, Germany) and a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE). Double-stranded complementary DNA was synthesized from 3–5 g total RNA using reagents recommended in the technical manual GeneChip Expression Analysis (Affymetrix, Santa Clara, CA). The in vitro transcription necessary for the synthesis of biotinylated complementary RNA (cRNA) was performed using the Enzo RNA Transcript Labeling kit (Affymetrix). The quality of cRNA was checked by hybridizing aliquots of the cRNA on a GeneChip Test3 array (Affymetrix). Fifteen micrograms of fragmented cRNA was hybridized to the Affymetrix HGU133A gene chip (Affymetrix). Hybridization was performed in a Hybridization Oven 640 (Affymetrix). Chips were washed and stained in the Fluidics Station 400 (Affymetrix), according SIGLEC-1 AS A TYPE 1 INTERFERON SURROGATE MARKER IN SLE 1139 Figure 1. A, Hierarchical clustering of monocyte transcriptomes (1,032 differentially expressed transcripts) from 7 normal donors (ND), including 2 replicate analyses (D1 and D2), and 9 patients with systemic lupus erythematosus (SLE), obtained using Genes@Work software. The 2 groups were strongly differentiated. The yellow boxed area shows a type I interferon (IFN)–regulated gene cluster consisting of 132 transcripts, which have all previously been described as targets for IFN. The blue boxed area shows a smaller section of the IFN signature, which includes sialic acid–binding Ig-like lectin 1 (Siglec-1) (red arrow). In addition to the well-known IFN target genes highlighted in yellow, newly identified coregulated genes (white boxes) are shown. An expanded view of the blue boxed area is shown on the right (as indicated by the blue arrow). B, Expression of Siglec-1 in normal donors and in patients with osteoarthritis (OA), rheumatoid arthritis (RA), ankylosing spondylitis (AS), systemic sclerosis (SSc), and SLE. Increased expression of Siglec-1 was found in monocyte transcriptomes from patients with SLE and patients with SSc. Horizontal bars show the mean. to procedure 2 described in the technical manual. Finally, the arrays were scanned with an Affymetrix GeneChip Scanner 3000. Statistical analysis of microarray data. Chip data were analyzed with GeneChip Operating Software (GCOS 1.1; Affymetrix). After global normalization and scaling to a trimmed mean expression height of 200, arrays from the control group (normal donors) were compared with the SLE samples, resulting in a total of 81 comparisons. Control group samples were also compared with other control group samples (n ⫽ 72), and SLE samples were also compared with other SLE samples (n ⫽ 72). To obtain a reliable list of differentially expressed genes, 2 independent filter strategies were applied; one for filtering more homogeneously regulated transcripts and the other for filtering more heterogeneously regulated transcripts. All transcripts fulfilling the criteria of 1 or both strategies were included in the final list of differentially expressed transcripts and were used for hierarchical cluster analyses. 1140 BIESEN ET AL Table 2. Potential biomarkers for type I IFN* Symbol Full name Fold change FCGR1A IFITM1 IFITM3 IL15RA LGALS3BP LY6E OAS2 PLSCR1 RTP4 SIGLEC1 TLR7 TMEM123 TNFSF10 Fc fragment of IgG, high-affinity Ia receptor (CD64) Interferon-induced transmembrane protein 1 (9–27) Interferon-induced transmembrane protein 3 (1–8U) Interleukin-15 receptor ␣ Lectin, galactoside-binding, soluble 3 binding protein Lymphocyte antigen 6 complex, locus E 2⬘,5⬘-oligoadenylate synthetase 2, 69/71 kd Phospholipid scramblase 1 Receptor (chemosensory) transporter protein 4 Sialic acid–binding Ig-like lectin 1 (sialoadhesin, CD169) Toll-like receptor 7 Transmembrane protein 123 Tumor necrosis factor superfamily 10 1.9 4.4 2.5 1.6 2.5 3.1 2.9 1.7 2.1 10.3 1.5 1.5 1.4 P 3.7 2.5 1.2 4.0 4.2 4.2 6.2 6.4 6.3 5.4 3.9 2.2 8.7 ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ ⫻ 10⫺20 10⫺25 10⫺24 10⫺11 10⫺15 10⫺11 10⫺17 10⫺13 10⫺17 10⫺14 10⫺15 10⫺20 10⫺8 * To identify potential surrogate biomarkers for type I interferon (IFN) that could be assessed by flow cytometry, we selected differentially regulated transcripts that were IFN inducible and encoded for surface proteins. Samples from 9 patients with systemic lupus erythematosus (SLE) were compared with 9 samples from 7 normal donors, and 81 signal log ratios (log2 of the fold change of 1 transcript on 2 different chips) were obtained. Samples were also compared within the group of normal donors and within the group of SLE patients. Fold change values for each transcript were calculated from 81 mean signal log ratios. P values were calculated using the t-test, which assumes different variability within the 2 groups of signal log ratios (the group of SLE patients versus normal donors, and the group of normal donors versus normal donors and SLE patients versus SLE patients). Filter strategy for the selection of more homogeneously regulated transcripts. The criteria for selection of homogeneously regulated transcripts included a mean expression signal in patients or controls of ⱖ100 (at a target intensity value of 200); ⬎0% “present” or “marginal” calls in patients or control groups; patient versus control percentage of “no change” calls ⬍70%; control versus control percentage of “no change” calls ⬎45%; and patient versus control fold change greater than all possible values (no filter parameter). In addition, the Welch t-test of log2 values of the fold change for patient versus control against control versus control had to yield a strict P value after Bonferroni correction (i.e., P ⬍ 1.47 ⫻ 10⫺8). Filter strategy for the selection of more heterogeneously regulated transcripts. The criteria for the selection of heterogeneously regulated transcripts included a mean expression signal in patients or controls of ⱖ50 (at a target intensity value of 200); ⬎0% “present” or “marginal” calls in patient or control groups; patient versus control “change” call percentage of “increase/marginal increase” or “decrease/marginal decrease” ⬎50%. Queries of both filter strategies were combined using the Boolean operator “and,” which allowed us to identify differentially expressed transcripts without using any foldchange cutoff. Thus, even transcripts that showed fold changes ⬍2, but were characterized by high significance values, could be identified. We compared the list of differentially expressed genes obtained by Significance Analysis of Microarrays (SAM; version 1.15) (35) with the list of 1,032 transcripts selected by our filter strategy and found an overlap of 97.7% (1,008 transcripts). SAM revealed many additional transcripts, which were not considered for further data interpretation since they were characterized by absent or no-change calls on GCOS analysis. For hierarchical clustering, Genes@Work software was used with gene vectors, and Pearson’s correlation coeffi- cient with mean or unit magnitude was used to measure similarity or distance (41). IFN-induced transcripts were identified by performing a literature search of PubMed using the medical subject heading term “type I interferon” and the official symbol in Entrez Gene for each transcript. Abstracts of the listed results were screened for plausibility. RESULTS Siglec-1 clusters in a prominent IFN signature in SLE monocytes. We analyzed transcriptomes of highly purified monocytes from 9 SLE patients and 7 normal donors and identified 1,032 differentially regulated transcripts. Cluster analysis performed with Genes@Work software revealed stringent discrimination between the 2 groups (42) (Figure 1A). Within the differentially expressed genes in the SLE group, a prominent cluster of 132 highly coexpressed transcripts, which have all previously been shown to be modulated by type I IFN, was identified. In addition to these transcripts, the cluster revealed other tightly coregulated genes, which have not previously been described as targets for type I IFN, but which are most likely IFN target genes, probably involved specifically in SLE pathophysiology. One of the most prominent differentially expressed genes within this signature, Siglec-1 (CD169), was chosen for further validation. In addition to SLE, we analyzed monocyte transcriptomes from patients with RA, AS, OA, and SSc for their expression of Siglec-1 and found it to be signifi- SIGLEC-1 AS A TYPE 1 INTERFERON SURROGATE MARKER IN SLE cantly overexpressed only in some patients with SSc (Figure 1B). Cytometric monitoring of Siglec-1 expression. Feasible biomarkers for IFN may become important in the management of SLE. Thus, we identified potential markers for type I IFN that could be assessed by flow cytometry by selecting transcripts encoding for surface proteins out of all IFN-regulated transcripts (Table 2). Siglec-1, an adhesion molecule that is normally restricted to macrophages, was highly up-regulated and therefore chosen for further validation in independent samples with flow cytometry. Siglec-1 expression was analyzed in inflammatory monocytes and resident monocytes from 52 SLE patients and 38 normal donors to test whether both monocyte subsets contribute to the IFN signature and to assess the correlation between Siglec-1 expression and clinical parameters, such as disease activity, complement levels, and levels of anti-dsDNA antibodies. Figure 2A shows a representative staining and an appropriate gating strategy for both monocyte subsets. Most patients with SLE had a significantly increased frequency of Siglec-1–positive inflammatory and resident monocytes compared with the levels in controls (Figure 2B). Siglec-1 is known to be expressed by tissue macrophages, but our findings showed that it was also expressed by circulating blood monocytes in SLE. Therefore, we analyzed whether other cell populations expressed Siglec-1. While gating to major leukocyte subsets in forward and side scatter on flow cytometry, we found it to be exclusively expressed on monocytes (data not shown). Siglec-1 is known to be up-regulated upon stimulation with IFN␣ in vitro (28). We investigated whether it was inducible in vivo by monitoring Siglec-1 expression before and during therapy with IFN␣2a in a patient newly diagnosed as having Erdheim-Chester disease (43). The patient received IFN␣2a (3 ⫻ 106 IU) subcutaneously 3 times per week. The frequency of Siglec-1–positive inflammatory monocytes increased from 15.3% to 93.5% and the frequency of Siglec-1–positive resident monocytes increased from 5.1% to 57.4% within 9 days after initiation of IFN␣ treatment. Frequency of Siglec-1–positive resident monocytes correlates with disease activity and with antidsDNA antibody titers. To assess the potential of Siglec-1 as a biomarker in SLE, we tested the correlation of clinical parameters with the frequency of Siglec-1–positive inflammatory and resident monocytes. While the frequency of Siglec-1–positive inflammatory monocytes and Siglec-1– positive resident monocytes both correlated with disease activity (as measured by the SLEDAI) (P ⫽ 0.006 and P ⫽ 0.005, respectively) and C3 levels (P ⫽ 0.0009 and P ⫽ 1141 Figure 2. A, Representative scatter plot of resident and inflammatory monocytes from a patient with active SLE (Systemic Lupus Erythematosus Disease Activity Index score 19). After gating both monocyte subsets, Siglec-1 was found to be highly expressed on both inflammatory and resident monocytes compared with negative controls. B, Percentages of positive inflammatory and resident monocytes in normal donors and SLE patients, as measured by flow cytometry in order to further validate up-regulation of Siglec-1. Broken line shows the calculated threshold frequency of Siglec-1–positive resident monocytes (24.5%) used to identify subjects with an activated IFN system. Percentages of Siglec-1– positive inflammatory monocytes were dramatically increased in most SLE patients (mean ⫾ SD 63.3 ⫾ 30.8%) compared with normal donors (19.6 ⫾ 21.3%). Percentages of Siglec-1–positive resident monocytes were also increased in the majority of SLE patients (38.0 ⫾ 24.04%) compared with normal donors (9.5 ⫾ 7.5%). Horizontal bars show the mean. P values were calculated by unpaired t-test. PE ⫽ phycoerythrin; APC ⫽ allophycocyanin (see Figure 1 for other definitions). 0.003, respectively), only the resident subtype showed a strong correlation with levels of anti-dsDNA antibodies (P ⫽ 0.052 for inflammatory monocytes and P ⫽ 0.0003 for resident monocytes) (Figure 3). Other clinical parameters, such as C4 levels and 1142 Figure 3. Correlation of clinical parameters with frequency of Siglec-1– positive monocyte subsets in patients with SLE. Pearson correlation coefficients were calculated for the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), C3 level, and titer of double-stranded DNA (dsDNA) antibodies (shown as log2 values). The frequency of Siglec-1–positive inflammatory and resident monocytes was correlated with disease activity (SLEDAI score) and was negatively correlated with the C3 level. The frequency of Siglec-1–positive resident monocytes, but not inflammatory monocytes, was significantly correlated with levels of anti-dsDNA antibodies. In 2 patients, the percentage of resident monocytes was too low for reliable cytometric analysis of Siglec-1 expression. Therefore, only 23 patients were included in the analysis of resident monocytes. See Figure 1 for other definitions. ESRs, were also correlated with the frequencies of both subpopulations. For C4 levels, P values were 0.019 for inflammatory monocytes and 0.045 for resident monocytes, and for ESRs, P values were 0.01 for inflammatory monocytes and 0.045 for resident monocytes. Leukopenia was correlated significantly with the frequency of inflammatory monocytes only, while no correlation was found between the frequency of either cell type and the CRP level (P ⫽ 0.20 for inflammatory monocytes and P ⫽ 0.78 for resident monocytes). Siglec-1 indicates activation of the IFN system and reflects IFN-suppressive therapy. A threshold value of Siglec-1–positive monocytes was needed to clarify activation of the IFN system in SLE patients. We used the frequency of Siglec-1–positive resident monocytes in BIESEN ET AL normal donors to calculate a threshold, because the variation of Siglec-1 expression was more homogeneous in resident monocytes (variance 56.7, coefficient of variation [CV] 79.6) than in inflammatory monocytes (variance 452.9, CV 108.3) (Figure 2B). The threshold was calculated by adding the mean percentage of Siglec1–positive resident blood monocytes in all healthy individuals (9.46%) to the 2-fold SD (SD 7.52%; 2⫻ SD 15.04%), which resulted in a threshold value of 24.5%. According to this criterion, 31 (65.6%) of 47 SLE patients had an activated type I IFN system, while only 2 (5.3%) of 38 normal donors fell in this range (Figure 2B). Five SLE patients had very low numbers of resident monocytes and therefore could not be analyzed for Siglec-1 expression. IFN-positive patients with SLE had a significantly higher mean SLEDAI score than did IFN-negative patients (7.07 versus 3.58; P ⫽ 0.01), and IFN-positive patients had increased mean levels of dsDNA antibodies compared with IFN-negative patients (282 units/liter versus 42 units/liter; P ⫽ 0.02). To determine whether Siglec-1 also reflects suppression of IFN, we monitored the rate of Siglec-1– positive inflammatory and resident monocytes in 4 patients with active SLE before therapy (day 0) and during therapy (day 7) with high-dose glucocorticoids. In all patients, frequencies of both Siglec-1–positive inflammatory monocytes and Siglec-1–positive resident monocytes were significantly reduced in the course of therapy (Figure 4). DISCUSSION We have identified Siglec-1 as a new biomarker for an activated type I IFN system in SLE. This is the first study to demonstrate a strong correlation between the frequencies of Siglec-1–positive monocyte subsets and clinical parameters, such as SLEDAI scores, complement factor levels, and titers of autoantibodies against dsDNA (which correlated only with the frequency of resident monocytes). Other than the correlation with titers of anti-dsDNA antibodies, our findings were consistent with the identification of other IFNinduced transcripts and their correlation with disease activity as described by Feng et al (9) and Kirou et al (8). Initial indications of the involvement of Siglec-1 in type I IFN–mediated pathophysiology arose in our global gene expression studies of purified monocytes from SLE patients and were comparable with previous findings in expression profiles generated with PBMCs from patients with SSc (24). With regard to SSc, York et al (28) recently described the up-regulation of Siglec-1 on peripheral blood SIGLEC-1 AS A TYPE 1 INTERFERON SURROGATE MARKER IN SLE 1143 Figure 4. Frequencies of Siglec-1–positive inflammatory monocytes and resident monocytes before and during glucocorticoid treatment. Four patients with active SLE received intravenous pulse glucocorticoids (methylprednisolone at 750 mg for 3 days and at 500 mg for 3 days). The frequencies of Siglec-1–positive inflammatory and resident monocytes were assessed on day 0 and after 7 days of therapy. The mean rate of Siglec-1–positive inflammatory monocytes decreased from day 0 to day 7 (77.6% versus 11.9%; P ⫽ 0.003 by paired t-test). The mean rate of Siglec-1–positive resident monocytes also decreased from day 0 to day 7 (62.3% versus 15.2%; P ⫽ 0.02 by paired t-test). Siglec-1 frequencies in both monocyte subsets reflected suppression of the effects of IFN by high-dose glucocorticoid therapy in all 4 patients. See Figure 1 for definitions. monocytes and demonstrated that Siglec-1 expression is amplified by agonistic stimulation via Toll-like receptor 3 (TLR-3), TLR-7, and TLR-9. In our monocyte-specific transcriptome analyses, we identified a subgroup of SSc patients expressing Siglec-1, thus confirming the results of the study by York et al. In addition to SLE and SSc, a type I IFN signature has also been identified in whole blood samples from a subgroup of RA patients (44). In the very limited number of RA patients analyzed in our study, no signs of an IFN␣ imprint were detectable. This discrepancy may result from a more homogeneous selection of patients with respect to disease activity, clinical symptoms, and medication. It has been shown that tumor necrosis factor ␣ (TNF␣) blockers may be indirectly responsible for a sustained type I IFN response, since natural TNF␣ negatively regulates IFN production by plasmacytoid dendritic cells and inhibits their differentiation from CD34⫹ progenitor cells (45). The pathophysiologic relevance of Siglec-1 expression in monocytes was demonstrated by cytometric analysis of monocytes from 52 patients with SLE and 38 healthy controls and the correlation of these data with clinical parameters. Most importantly, and in contrast to previously validated IFN biomarkers in SLE, we found a high correlation between titers of anti-dsDNA antibodies and the frequency of Siglec-1–positive resident monocytes, while there was no significant correlation with the frequency of Siglec-1–positive inflammatory monocytes. This finding suggests a potential involvement of Siglec-1– positive resident monocytes in autoantigen presentation. Since it is known that SLE monocytes gain antigen-presenting capabilities after stimulation with IFN␣ (22), it appears reasonable to assume that resident monocytes, rather than inflammatory monocytes, are responsible for the induction of autoantibodies. This assumption is supported by the findings of Ancuta et al (46), showing that resident monocytes are more likely to differentiate into dendritic cells than are inflammatory monocytes. Muerkoster et al (47) demonstrated that Siglec-1–positive macrophages present antigens to T cells and are necessary to induce a graft-versus-leukemia reaction in mice. Taken together, these findings strongly suggest the need for a more detailed analysis of resident monocytes and their capacity to present autoantigens in a Siglec-1–dependent manner. In the present study, we analyzed titers of anti-dsDNA antibodies only, but other autoantibodies, such as anti–ribosomal P or anti-Sm, may be induced by the same mechanism. Siglec-1 has been shown to be inducible by IFN␣ in vitro (26,28). In the present study, we showed that Siglec-1 was induced in vivo in response to IFN treatment in a patient with Erdheim-Chester disease. Siglec1–positive monocytes had previously been found in SLE patients with renal involvement and in patients with HIV (29,30). Ikezumi et al (48) found Siglec-1–positive macrophages in kidney sections from patients with lupus nephritis. Interestingly, they also showed that reduction in the number of glomerular Siglec-1–positive macrophages correlated with the response to glucocorticoid therapy and with reduced proteinuria and glomerular lesions. This indicates a potential role of Siglec-1– positive macrophages in mediating inflammationinduced tissue damage in lupus nephritis. Correlation 1144 BIESEN ET AL between the number of Siglec-1–positive macrophages and disease activity–dependent parameters such as proteinuria is consistent with our finding of a correlation between frequencies of Siglec-1–positive monocyte subsets and the SLEDAI score. The usefulness of Siglec-1 as a biomarker for monitoring therapeutic modulation of type I IFN responses was additionally supported by the finding that high-dose glucocorticoid treatment of active SLE was accompanied by a drastic down-regulation of Siglec-1 on inflammatory and resident monocytes. This finding not only reflects the capacity of high-dose glucocorticoid therapy to suppress IFN, but also implies an abrogation of the antigen-presenting capacity of Siglec-1–positive resident monocytes (49,50). Glucocorticoid treatment may possibly be displaced or supplemented in the future by new therapeutic approaches targeting IFN. SLE patients may benefit from new IFN-suppressive therapies, but long-term use of these experimental treatments may also entail risks. Strong suppression of IFN may result in an increased incidence of tumors or overwhelming viral infections by interfering with the protective functions of the type I IFN system (51). However, insufficient suppression of IFN may result in new flares and ongoing damage in SLE patients. Therefore, individualized adjustment of the degree of IFN suppression into a therapeutic range appears to be of prime importance. A threshold is required to identify patients with an activated IFN system who may benefit from new approaches targeting IFN. Using the proportion of Siglec-1–expressing resident monocytes in normal donors, we calculated that a frequency of ⬎25% Siglec-1–positive resident monocytes indicates an IFN response in SLE patients. Further studies with larger cohorts of patients are needed to determine whether this threshold is a reliable marker to indicate an activated type I IFN system. In summary, these results present a new biomarker for monitoring disease activity in SLE. In addition, our findings suggest a causal link between Siglec-1 expression in resident monocytes, antigen presentation, and titers of anti-dsDNA antibodies in the pathophysiology of SLE, which warrants further study. ACKNOWLEDGMENTS The authors acknowledge Beate Möwes and Heidi Schliemann for excellent technical assistance and Vanessa Tatum for critical reading of the manuscript. AUTHOR CONTRIBUTIONS Dr. Gruetzkau had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study design. Radbruch, Hiepe, Burmester, Grützkau. Acquisition of data. Biesen, Demir, Barkhudarova, Backhaus, Rudwaleit, Riemekasten, Burmester. Analysis and interpretation of data. 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