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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.

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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
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).
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:
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
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
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-
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.
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
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
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)
White, no. (%)
48 (92.3)
Asian, no. (%)
4 (7.7)
No. of ACR criteria fulfilled, mean
5.1 (4–8)
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)
* 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 [38] score 5.3 ⫾ 1.6), and mobility was
restricted (mean ⫾ SD Bath Ankylosing Spondylitis Metrology
Index [39] 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
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
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.
Table 2. Potential biomarkers for type I IFN*
Full name
Fold change
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
* 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
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.
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
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-
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 ⫽
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
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
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).
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
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
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.
The authors acknowledge Beate Möwes and Heidi
Schliemann for excellent technical assistance and Vanessa
Tatum for critical reading of the manuscript.
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. Biesen, Demir, Grün, SteinbrichZöllner, Hiepe, Burmester, Grützkau.
Manuscript preparation. Biesen, Häupl, Riemekasten, Burmester,
Statistical analysis. Biesen, Grün, Häupl.
Overall project management. Radbruch, Burmester.
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