Published in Volume
113, Issue 12
(June 15, 2004)J Clin Invest.
Copyright © 2004, American Society for Clinical
Characterization of heterogeneity in the molecular pathogenesis of
lupus nephritis from transcriptional profiles of laser-captured
1Department of Pediatrics, Columbia
University, New York, New York, USA.
2Johnson & Johnson
Pharmaceutical Research and Development, San Diego, California, USA.
3Department of Pathology, Columbia University, New York, New York,
4OmniViz Inc., Maynard, Massachusetts, USA.
Address correspondence to: Karin S. Peterson, Department of Pediatrics,
Columbia University, PH4-477, 630 168th Street, New York, New York 10032,
USA. Phone: (212) 305-5766; Fax: (212) 305-9078; E-mail:
Published June 15, 2004
Submitted: June 6,
2003; Accepted: April 9,
The molecular pathogenesis of focal/diffuse proliferative lupus
glomerulonephritis was studied by cDNA microarray analysis of gene expression in
glomeruli from clinical biopsies. Transcriptional phenotyping of glomeruli
isolated by laser-capture microscopy revealed considerable kidney-to-kidney
heterogeneity in increased transcript expression, resulting in four main gene
clusters that identified the presence of B cells, several myelomonocytic
lineages, fibroblast and epithelial cell proliferation, matrix alterations, and
expression of type I IFN–inducible genes. Glomerulus-to-glomerulus
variation within a kidney was less marked. The myeloid lineage transcripts,
characteristic of those found in isolated activated macrophages and myeloid
dendritic cells, were widely distributed in all biopsy samples. One major
subgroup of the samples expressed fibrosis-related genes that correlated with
pathological evidence of glomerulosclerosis; however, decreased expression of
TGF-β1 argued against its role in lupus renal fibrosis. Expression
of type I IFN–inducible transcripts by a second subset of samples
was associated with reduced expression of fibrosis-related genes and milder
pathological features. This pattern of gene expression resembled that exhibited
by activated NK cells. A large gene cluster with decreased expression found in
all samples included ion channels and transcription factors, indicating a
loss-of-function response to the glomerular injury.
Systemic lupus erythematosus (SLE) is a genetically determined autoimmune disease in
which a T cell–driven adaptive immune response results in the formation
of autoanti bodies, most notably directed against DNA, RNA, and their associated
proteins (1, 2). Classically, the resulting immune complexes have been considered to
initiate renal disease, one of the most significant causes of morbidity and
mortality in lupus, through complement activation in a neutrophil-mediated
Arthus-type reaction. However, evidence from studies of spontaneous lupus in the New
Zealand Black/White (NZB/W) mouse has indicated involvement of other pathways,
including activation of myeloid cells via Fc receptor engagement, that are thought
to mediate the inflammatory response (3).
The pathological manifestations of lupus nephritis are diverse, variably affecting
the different renal compartments, including glomeruli, tubules, interstitium, and
vasculature (4). Lupus glomerular injury takes
several forms and is categorized into five classes: WHO classes I–V
(4). A major feature of lupus nephritis is
the considerable degree of variation in severity of histopathological involvement
between different glomeruli from the same biopsy, and the histopathological
classification is therefore based on the average score for all glomeruli in the
biopsy. The most common and most severe forms, focal (class III) and diffuse (class
IV) proliferative glomerulonephritis, share similar histopathological features and
differ principally by the proportion and extent of glomerular involvement.
Importantly, the course, response to therapy, and outcome are heterogeneous among
affected individuals, with nearly one in three progressing to glomerulosclerosis and
end-stage renal disease, while the others either respond to therapy or follow a
milder course (4). These differences in
outcome are not well correlated with WHO class (4). Efforts at better predicting outcome and therapeutic requirements from
biopsy morphology were initiated by Pollack et al., whose distinction of active and
sclerosing lesions led to the development of the activity and chronicity indices
(5, 6). Although there is a positive correlation of elevated chronicity index
with progression to chronic renal insufficiency, the lack of a clear threshold
value, low sensitivity, and problems in reproducibility have limited the utility of
the morphological indices (7). These points
raise the question of whether there are additional differences not identified at the
level of conventional histopathology that are relevant to the nature of the renal
injury and emphasize the need to analyze the pathogenesis of lupus nephritis at the
The application of the microarray technique to the monitoring of gene expression
profiles has provided significant insights into the biology of different neoplasms
and has demonstrated its value in the approach to their subclassification (8). However, there are significant problems
raised by the application of this methodology to lupus nephritis. First, the
architecture of the kidney is complex and different structural regions of the kidney
exhibit distinctive gene expression profiles (9). Because lupus nephritis affects each compartment of the kidney in
different ways, with the most profound primary immune complex injury at the level of
the glomerulus, it is advantageous to study glomerular gene expression separately
from that of the interstitium and tubules. Second, the potential for
inflammation-related alterations in multiple renal cell lineages and the presence of
infiltrating inflammatory cells complicate the interpretation of the results.
Finally, only the limited “archival material” remaining
after diagnostic renal biopsy interpretation is available for research purposes,
constraining the scope of potential studies. The technology described by Luo et al.
(10) that integrates laser-capture
microscopy of individual cells with linear RNA amplification and cDNA microarray
analysis offers an approach for overcoming these limitations. This combined
technology recently has been applied to the analysis of malignant and premalignant
tissues (11–13). In the present study, we first
sought to determine the feasibility of using laser-captured human glomeruli to
examine the gene expression profiles of archival clinical lupus biopsies. In
addition, we wished to determine whether microarray analysis could assist in the
identification of molecular processes underlying lupus glomerular disease, with the
ultimate goal of identifying molecular pathways and their heterogeneity that may be
responsible for specific disease features and their relationship to the autoimmune
Hierarchical clustering of genes differentially expressed in SLE glomeruli
compared with controls. Glomeruli were isolated successfully from the surrounding tissue by laser-capture
microscopy, as documented by microscopic visualization of glomeruli before and
after laser capture (Figure 1), and the
transcriptional phenotypes of the glomeruli were determined using amplified RNA
in microarray analyses. Compared with the mean value of expression in controls,
SLE glomeruli exhibited one large cluster of genes with increased expression and
one with decreased expression in each of two independent and separately
processed and analyzed sets of lupus glomeruli studied on different cDNA
microarrays (Figure 2A). Because of the
high degree of similarity between the corresponding separate hierarchical
clusters, the 88 genes with increased expression in the two data sets were
merged and analyzed by hierarchical clustering (Figure 2B). Four main gene clusters with increased expression
were identified (Supplemental Table 1;
supplemental material available at
http://www.jci.org/cgi/content/full/113/12/1722/DC1) and analyzed in detail (see
below) for identification of their possible biological significance. Although
the number of cDNAs on the microarray was limited and not inclusive of all
possible genes in the clusters, we were able to discern biological
characteristics likely related to function in each cluster. Briefly, seven of
the eleven genes in cluster I were characterized by the presence of type I IFN
response elements. Approximately half of the genes in cluster II contained
transcripts that indicated the presence of cells of the myelomonocytic lineage
in the glomerulus, and two of four genes in the small cluster III were expressed
specifically in B cells. Cluster IV was characterized by the fact that more than
one third of the transcripts were involved in the production or regulation of
extracellular matrix (ECM), and this cluster appeared likely to be linked to the
glomerulosclerosis (fibrosis) of lupus nephritis. Each transcript has been
identified by its corresponding gene symbol as contained in LocusLink, which
also includes the primary references to individual genes (14).
Laser-capture microdissection. (A_C) Isolation of a
glomerulus by laser-capture microdissection from a hematoxylin and
eosin_stained section of a lupus renal biopsy cut 7 μm in
thickness. The arrow in A shows a glomerulus prior to capture.
Activation by laser of a heat-sensitive film, placed in direct contact with
the tissue section, causes the cells located in the path of the laser to
adhere to the film, and they can be lifted out of the tissue section.
C indicates the successful film transfer of the captured
glomerulus used for RNA extraction. B demonstrates the
remaining biopsy after laser capture of the glomerulus. Magnification,
Gene expression profiles of lupus glomeruli. (A and
B) Hierarchical clustering analysis of 25 lupus glomeruli,
isolated from 12 different SLE biopsies, and 6 control glomeruli, isolated
from 4 different control kidneys. Each row corresponds to a cDNA on the
microarray and each column, to a glomerular sample. Along the tops of
A and B, each lupus sample is identified by a
number in which the part before the dash identifies the biopsy and the part
after the dash identifies the individual glomerular sample. In
A, controls (*C) are shown and are identified by
number as described above. The linear intensity scale ranges from fourfold
or higher (+4) to minus fourfold or lower (_4). A positive
“fold change” (yellow to red) indicates increased
expression and a negative “fold change” (light to
dark blue) indicates decreased expression in a glomerular sample compared
with the average of the controls. A shows hierarchical
clustering of 88 genes with increased expression and 89 genes with decreased
expression in lupus glomeruli compared with the average of the controls,
analyzed independently in sample set B12 and sample set B13. The location of
some cDNA rows is indicated by their respective gene symbol along the right
side of the clustering diagram. Supplemental Table 1 lists all genes that were clustered in
A. B shows the combined hierarchical clustering of
all genes with increased expression in lupus glomeruli in sample set B12 and
B13. Four main gene clusters are shown (left margin) and are labeled as
described in Results.
Immuno- and histopathology summary for each glomerulus studied
The large cluster of genes with decreased expression in lupus glomeruli, compared
with that of controls, was in general uniformly decreased across all samples
(Figure 2A). The transcripts included
transcription factors and ion channels or were involved in aspects of cellular
growth and differentiation (Supplemental Table 1). Some genes encoded molecules whose function in the kidney is not
established. Unexpectedly, given their increased expression in some forms of
glomerulosclerosis other than lupus (15,
16), both TGF-B1 (TGF-β1)
and two of its receptor molecules, TGF-BR2 and TGF-BR3, exhibited significantly
decreased expression in SLE glomeruli compared with controls, as did plasminogen
activator inhibitor type 1 (PAI-1or SERPINE; Figure 2A). Of note also was the decreased expression of
transcripts involved in endothelial proliferation and angiogenesis, including
VEGF, VEGFR1 (Flt1), and FGF1, particularly as endocapillary proliferation is a
morphologic characteristic of class III/IV lupus nephritis (Table 1).
Gene expression profiles of glomeruli isolated from the same biopsy cluster
together. As illustrated by the sample dendrogram in Figure 2B, different glomeruli obtained from the same patient biopsy were more
concordant in their overall expression profiles than were glomeruli from
different biopsies. Notably, in the case of biopsies 9 and 38, the gene
expression profiles of glomeruli from the same biopsy clustered together even
when analyzed in the independent data sets B12 and B13, despite some differences
in expression intensity. The paired glomeruli from biopsies 69, 109, and 114
were also concordant in their overall gene expression. Conversely, the paired
glomeruli from biopsies 68, 87, and 90 shared the expression of certain clusters
but differed markedly in the expression of others, notably those of the fibrosis
Increased expression of genes with type I IFN response elements. Gene cluster I comprised 11 transcripts, most of which contained type I IFN
response elements, and included G1P2 (ISG15), a ubiquitin-like molecule, and
IFIT1, encoding the intracellular p56 protein, which inhibits protein synthesis.
MX1 and MX2 are two large GTPases that belong to the dynamin family of
microtubule-binding proteins; their specific function in man is not known in
detail. MNDA (myeloid cell nuclear differentiation antigen) encodes a nuclear
protein that promotes differentiation of the myeloid lineage, and SP100, a
nuclear autoantigen and transcriptional enhancer. KIAA1268 exhibits significant
DNA sequence homology to an IFN-α–induced gene
identified in the rainbow trout (17).
Only thromboxane A2 receptor (TBXA2R), adenylosuccinate synthase (ADSS),
prostasin (PRSS8), and the NCK adaptor protein 1 (NCK1) in this cluster lack IFN
A cell reference panel was used in an effort to identify patterns of infiltrating
cell lineages among the glomerular expression profiles. A number of resting and
activated peripheral blood cell cultures had been analyzed by microarray, and
their expression profiles were linked by their gene identities to those of the
glomerular samples, as shown in Figure 3.
The top part of Figure 3 illustrates the
overlap in expression between the transcripts of cluster I and the peripheral
blood reference panel and demonstrates a strong expression of all cluster I
genes by LPS-activated NK cells (CD56), but not by unstimulated NK cells. Some
of the transcripts with type I IFN response elements were also expressed at
lower levels by activated CD4 cells and cells of the neutrophil lineage.
Overlapping gene expression profiles between a peripheral blood cell
reference panel and lupus glomeruli. Shown is the hierarchical clustering of
transcripts expressed by resting and activated cells (cells listed at top)
obtained from the peripheral blood of normal donors. Only genes that were
increased in expression in lupus glomeruli (Figure 2B) were selected for clustering from the
expression profiles of the peripheral blood cell reference panel. Each row
represents a gene, and some gene names are listed along the right side of
the figure. The gene rows have been subdivided on the left side of the
figure according to the four main gene clusters (I_IV) identified in Figure
2B and described in Results. The
linear intensity scale is the same as described in Figure 2. act., activated.
Increased expression of genes related to myelomonocytic and other
inflammatory cells. Cluster II was the most widely distributed cluster expressed by SLE glomeruli.
Myelomonocytic cell lineage marker genes characterized this cluster and included
CD14, CD40 (TNFRSF5), CD18 (ITGB2), CD53, C3 receptor (C3AR1), Toll-like
receptor 2 (TLR2), and two components of the microbicidal oxidase system, NCF2
and CYBB. The mitogen-induced chemokine MIP-1-α (CCL3), involved in
the recruitment and activation of various inflammatory cells, was a highly
expressed chemokine, along with Eta-1 (osteopontin; SPP1), a cytokine that is
involved in early activation by T cells.
Pathways involved in the cellular uptake of antigen and immune complexes were
suggested by a set of transcripts including CD36, CD163, FCGBP, and FCER1G. CD36
is a thrombospondin receptor that also functions as a scavenger receptor (18), and CD163 is a putative scavenger
receptor present on myeloid cells. FCGBP is a mucin-rich IgG Fc receptor
(FcγR) and FCER1G (FcRγc) is the signaling
γ-chain that is common to several of the Fc receptors (FcRs). Also
increased in expression were LYN and HCK, encoding two Src family kinases that
are activated upon FcγR stimulation (19). Several transcripts indicated activation of the lysosomal
antigen presentation pathway, including the lysosomal enzymes legumain (LGMN),
cathepsin B (CTSB), cathepsin H (CTSH) and IFI30. The last encodes an
IFN-γ–induced lysosomal thiol reductase (GILT) whose
expression is essential for antigen presentation by MHC class II molecules and
normal immune function (20).
It is clear from the gene expression profiles that the myeloid-related
transcripts vary in their expression across lupus glomeruli, although elements
of cluster II were present in almost all samples (Figure 2B), suggesting a heterogeneous pattern of
infiltrating myeloid cells of different lineages and activation states.
Additional evidence for the potential presence of a diverse set of myeloid cells
was obtained by analyses of the parallels in expression between known genes in
cluster II and the cell reference panel (Figure 3, II). Expression profiles characteristic of activated and resting
neutrophils, monocytes, and dendritic cells could be identified among
lupus-associated glomerular transcripts. However, individual cell lineages could
not be assigned with certainty, as only a few transcripts were specific for an
individual cell type; for example, Eta-1, expressed by dendritic cells.
Few transcripts were specific for T cells, among them MAL, a marker of the
differentiated T cell lineage, which clustered with the myeloid transcripts.
Analysis of expression of the lupus-associated transcripts by the peripheral
blood reference set also demonstrated a paucity of T cell profiles in glomeruli,
and the activated CD4 cell was the only lineage that exhibited considerable
overlap in gene expression (Figure 3).
The small but distinctive gene cluster III mainly reflected the presence of
transcripts identifying immunoglobulin heavy (IGHG3) and light (IGL) chains and,
as illustrated by Figure 3, this cluster
was specifically expressed by B cells in the reference panel.
Increased expression of genes related to ECM and glomerulosclerosis. Gene cluster IV contained a set of transcripts known to be involved in the
production or regulation of ECM that are likely to be involved in elements of
the inflammatory response leading to lupus renal fibrosis and included lumican
(LUM), collagen I and VI (COL1A2 and COL6A3), and matrilysin (MMP7). Epithelial
proliferation and increased numbers of fibroblasts are additional features of
fibrosis, and several transcripts in cluster IV indicated the presence of these
two processes: desmoplakin (DSP) is involved in the formation of epithelial
sheets and keratin 18 (KRT18) is an intermediate filament of simple epithelia.
THY1 is a fibroblast marker and FAP is a fibroblast-activating protein not
expressed by normal adult tissue but synthesized by reactive stromal fibroblasts
in healing wounds. Interestingly, this cluster also contains CCL2, encoding
monocyte chemoattractant protein 1 (MCP-1), a chemokine previously linked to
renal fibrosis (21). Other transcripts
encode proteins that are related to inflammation and not known to be directly
involved in the process of fibrosis, such as the arachidonate
5-lipooxygenase-activating protein (ALOX5AP), complement C3, C1r (C1R), and I
factor (IF), and a hematopoetic transcription factor, RUNX1. Cathepsin C (CTSC)
was highly and consistently present among these transcripts. Except for CCL2,
which is expressed mainly by activated monocytes, and RUNX1,
the fibrosis gene cluster contained few genes that were also expressed by cells
in the blood reference panel (Figure 3,
IV), suggesting that the majority of the cluster IV transcripts were derived
from parenchymal cells. The source of MCP-1 (CCL2) is ambiguous, as the
mesangial cell has been shown to synthesize this chemokine after exposure to
inflammatory or mechanical injury (22).
Characterization of molecular heterogeneity among lupus glomeruli. Although all lupus biopsies in this study were classified as WHO class III or WHO
class IV and in all but two instances were obtained from patients with clinical
renal disease with a duration of 1 month or less (Table 2), they exhibited a distinct heterogeneity in gene
expression, as illustrated in Figure 2B.
Analysis of the variance in gene expression between biopsies, with grouping of
all glomeruli from each biopsy, using a nested ANOVA algorithm (23) (data not illustrated) identified two main biopsy
subgroups, designated SG1 and SG2. Hierarchical clustering of transcripts
differentially expressed by these two subgroups demonstrated that biopsies in
the SG1 group were characterized by strong expression of several genes in the
fibrosis cluster, whereas the SG2 biopsies were distinguished by high expression
of most of the transcripts previously identified as containing type I IFN
response elements and low expression of the fibrosis-related genes that
characterized SG1 biopsies (Figure 4).
Differential gene expression between two major subgroups of lupus glomeruli.
Shown is the hierarchical clustering of genes with statistically significant
difference in expression between the two major subpopulations of lupus
glomeruli, SG1 and SG2, identified in this study. Lupus glomerular samples
are listed along the top (identified as in Figure 2), and each row identifies a gene, labeled with
its gene symbol on the right side of the figure. The linear intensity scale
is the same as described in Figure 2.
The expression levels in control glomeruli of the key transcripts that
define the SG1 and SG2 lupus subpopulations are illustrated as described in
Clinical data for SLE patients studied by renal biopsy
Molecular heterogeneity was also examined at the level of the individual
glomerulus. A scoring system was used to determine the presence or absence of a
specific gene cluster to each glomerular sample (Figure 5). Transcripts with type I IFN response elements were
concordantly expressed by all glomeruli in seven of twelve biopsies and were
absent in all glomeruli in the remaining five samples. All but one glomerulus
across all twelve biopsies were classified as expressing the cluster II
(myeloid) transcripts; however, the expression level was variable, as seen in
Figure 2B, and different glomeruli from the
same biopsy were not always concordant in their expression of particular
transcripts. The expression pattern of a myeloid subcluster that included CD14,
FCER1G, IFI30, and CD163, among others (Supplemental Table 1), showed more heterogeneous distribution, being
present in 18 of 25 glomeruli (Figure 5).
Expression of the fibrosis cluster was assigned to 16 glomeruli (9 of 12
biopsies), with only one example (sample 87) of discordance between two
glomeruli from the same biopsy. Expression of the B cell cluster was found in 16
glomeruli and partially overlapped expression of the fibrosis-related genes.
Three of eight biopsies with two or more glomeruli diverged in expression of B
Summary of expression pattern of gene clusters in each glomerular sample.
Presence (shaded square) or absence (open square) of the expression of a
gene cluster was determined by whether the average expression of the genes
in each cluster or subcluster exceeded an increase of 1.5 fold relative to
the average expression in the control glomeruli.
Validation of microarray expression by quantitative real-time PCR. In order to determine whether the expression of transcripts obtained by the
microarray analysis could be independently validated by a method involving
neither linear mRNA amplification nor microarray analysis, we performed
quantitative real-time PCR (qRT-PCR) to independently quantify the relative
abundance of mRNAs. For qRT-PCR, glomeruli were separately isolated by
laser-capture microscopy. The RNA was extracted and converted to cDNA without
prior amplification of RNA. Primers for CTSC, ISG15, and MX1 were selected
because the expression of these transcripts differs significantly among samples
(Figure 4), and the primers were used to
amplify the respective cDNAs by qRT-PCR. For each renal sample, the level of
expression of these transcripts was determined relative to the expression of a
control transcript, eukaryotic elongation factor 1 α-1 (EE1A1),
amplified in a parallel qRT-PCR reaction. From each biopsy, four to nine
replicates from one or more glomeruli were obtained and were analyzed separately
by qRT-PCR. The linear regression line fit to the average expression of CTSC,
ISG15, and MX1 determined by qRT-PCR or microarray analysis for each of the
samples and each of the genes is shown in Figure 6 and illustrates that the two methods of analysis correlate
significantly even though different glomeruli from the respective biopsies were
used in each method; the Spearman correlation coefficient equals 0.754
(P = 0.0001) in all samples studied.
This scatter plot depicts the linear regression line fit to the average
expression of CTSC (squares), ISG15 (triangles), and MX1 (stars) determined
by qRT-PCR or microarray analysis for each of the samples (indicated by the
respective biopsy number) and each of the genes. The CT cycle method was
used to calculate the amplification of the genes by qRT-PCR relative to that
of the reference control gene EEF1A1. The x axis is the
mean of the logarithm of the intensities in the microarray experiment used
for clustering for each of the tested genes in each sample. The two methods
of analysis correlate significantly; the Spearman correlation coefficient is
0.754 (P = 0.0001). The 95%
confidence intervals of the regression line for individual data are
Relationship of molecular phenotype with histopathological studies. Study of the relationship between the presence or absence of a specific gene
cluster with the immunopathological findings of the biopsies revealed no overall
correlation between activity and chronicity indices and gene expression (Table
3). However, there were a number of
correlations with elements of the indices and individual gene clusters. All
biopsies were also studied by immunohistochemistry using CD3 and CD68 staining
(Table 1 and Figure 7), and these features of the biopsy were included in
the correlations with individual gene clusters.
Immunohistochemical staining of a lupus renal biopsy. (A and
B) Monoclonal antibodies identifying CD68 (A)
and CD3 (B) were used to stain lupus biopsies, followed by a
second antibody and detection with peroxidase. Similar staining of control
biopsies did not identify any reactive cells (data not shown).
Correlation of molecular phenotype with histopathology
The expression of the genes with type I IFN response elements was associated with
lower scores for three elements of the activity and chronicity indices:
neutrophil infiltration, cellular crescents, and fibrous crescents. Moreover,
these glomeruli were characterized by significantly smaller infiltration of CD3
T cells by histological scores. In contrast, renal biopsies containing glomeruli
classified as expressing the fibrosis-related transcripts were distinguished by
higher scores of the activity and chronicity indices, including cellular
crescents, fibrous crescents, and wire loops. However, the presence of the
fibrosis cluster was not uniformly associated with higher scores, as there was a
trend toward decreased occurrence of necrosis. Interestingly, the expression of
B cell genes by glomeruli paralleled the extent of histologic infiltration by
CD3 T cells. It also correlated with the presence of cellular crescents, fibrous
crescents, and interstitial inflammation. Table 3 additionally illustrates that while the total myeloid gene cluster
was not significantly associated with any individual histopathological feature,
the CD14-containing myeloid subcluster correlated with elements of the activity
index, most notably endocapillary proliferation, wire loops, and cellular
crescents, but not with necrosis. The expression of this subcluster also
correlated with increased presence of CD68+ cells by histological scores and a higher activity index. Differences in
the clinical and laboratory features (Table 2) of the patients corresponding to the SG1 and SG2 subgroups of gene
expression (Figure 4) did not account for
these differences. In particular, the median duration of nephritis was the same
in the two groups.
The combination of laser-capture isolation and microarray analysis allowed the use of
“archival” renal biopsies to study molecular events involved
in lupus glomerular disease and averted interference from distinct pathological
events occurring in extraglomerular structures such as the renal interstitium and
tubules. This work demonstrated that regularly processed clinical biopsies could be
used if they were frozen within 1–2 hours of being obtained. Given the
minute quantities of RNA obtained, linear amplification of RNA was an essential step
for transcript quantification by microarray analysis. There is considerable support
for the sensitivity and reproducibility of laser-capture microdissection and T7
promoter–based RNA amplification in the detection of transcription
profiles even down to the single-cell level (24). In the present study, two lines of work supported the consistency of
the results. First, the similarity of findings between two semi-independent data
sets included in this analysis (Figure 2)
allowed a degree of cross-validation showing that technical factors had minimal
influence and supporting the choice to pool the two datasets for the main analysis.
Second, the level of transcript expression for three informative genes was
independently validated by qRT-PCR analysis using nonamplified RNA as the starting
material, with analyses by the two methods correlating significantly (Figure 6).
A dominant feature of this study was the considerable degree of heterogeneity between
lupus kidney biopsies in the increased expression of four main gene clusters, with
each gene cluster reflecting the contribution of different pathways relevant to
glomerular inflammation. These divergent features appear highly inconsistent with a
unitary pattern of Arthus reaction–mediated necrosis and indicate that
several different pathogenic elements are variably present in lupus nephritis.
Interestingly, there was relatively less glomerulus-to-glomerulus molecular
heterogeneity within a kidney than between kidneys, despite the variation in
glomerular histopathological involvement within a kidney. We anticipate that the
differences in these molecular patterns found in different kidneys will elucidate
some of the marked heterogeneity in clinical outcome of lupus glomerulonephritis,
but the design of this study did not allow this question to be addressed. However,
the potential significance of each gene cluster to different pathological aspects of
lupus nephritis was supported by the finding of associations of particular clusters
with some elements of the activity and chronicity indices. No association was found,
however, between transcriptional profiles and the WHO class III and IV subdivision
of glomerulonephritis, indicating that similar molecular pathogenic processes
characterize the two classes and that both exhibited equivalent heterogeneity in
their transcriptional phenotype. A number of genes were consistently reduced in
expression across all glomeruli, including ion channels and transcription factors,
suggesting a loss-of-function response to the glomerular inflammation.
The observed heterogeneity in gene expression among glomeruli in different biopsies
(Figures 2 and 4) raises the important question of whether these differences identify
subtypes of lupus nephritis or are a reflection of different stages in the
progression of lupus nephritis. In support of the former possibility, both the SG1
and SG2 subgroups (Figure 4) had an
equivalently high proportion of class IV proliferative nephritis, similar deposition
of immune complexes and complement components, and in serum, similarly elevated
antibodies to DNA and lowered complement levels (Table 2). The identical, relatively short mean duration of
disease in both the SG1 and SG2 subgroups and comparable therapy in both groups
prior to biopsy argues that the observed differences in transcriptional phenotype
mainly reflect heterogeneity in the mechanisms of proliferative glomerulonephritis.
However, serial gene expression profiles should be studied in consecutive renal
biopsies to define more precisely the changes that occur in time. The trend toward
mean higher serum creatinine levels in individuals with the SG1 classification of
gene expression probably reflected a consequence of the processes occurring in the
The use of microarrays is a powerful tool for hypothesis generation that can lead to
false-positive as well as false-negative conclusions, so it is imperative to
cross-validate the present findings in a new study comprising a totally independent
cohort of lupus glomeruli. It should be stressed, however, that the criteria used to
select genes for clustering in this study were intentionally conservative, such as
the requirement of a twofold change of expression in at least one third of lupus
samples and control of the false discovery rate to less than 0.05. This resulted in
the identification of fewer genes but at correspondingly greater levels of
The most widespread pattern of elevated gene expression in lupus glomeruli was for a
large cluster of transcripts identified as being expressed mainly by cells of the
myeloid lineage. We attempted to identify the particular myeloid lineages present in
the glomerulus by using a peripheral blood reference panel and obtained evidence
that was compatible with the presence of activated monocytes/macrophages, activated
dendritic cells, and activated neutrophils (Figure 3). However, it was not possible to clearly distinguish the lineages present
in the glomeruli due to the large number of genes with overlapping expression among
the various myeloid cell types. These findings emphasize the importance and the
complexity of the myeloid lineages found in the nephritic kidney and they are
consistent with observations in the NZB/W mouse model of spontaneous lupus-like
nephritis (3, 25).
The myeloid subcluster that included CD14 exhibited more variation in glomerular
expression than the myeloid cluster and correlated with certain features within the
activity index, including cellular crescents, endocapillary proliferation, and wire
loops, but not with others, such as necrosis (Table 3), which suggests that a particular subset or activation pattern of
macrophages accounts for certain features present in active lupus nephritis. The
transcriptional signature of the myeloid subcluster also correlated with
immunohistochemical staining by CD68, providing an additional cross-validation of
the microarray results. These findings are consistent with the proposed inclusion of
increased macrophage numbers in a modified activity index proposed by Hill et al.
(7) and emphasize the importance of this
lineage in lupus glomerulonephritis.
Signaling through the activating FcRγc chain is central to the immune
complex–mediated glomerular injury that occurs in the NZB/W mouse model
of lupus nephritis (3). It was therefore
especially interesting to find an increase in the expression of FcRγc in
the CD14 myeloid subcluster, suggesting that a similar pathway of macrophage
activation may occur in the human disease. Expression of the
“classic” FcRs that signal through FcRγc has
been difficult to demonstrate in the glomerulus and is an area of investigation
(26). In this context, it is intriguing
to find increased expression of FCGBP (Fcγ-binding protein), an FcR
previously unknown in the kidney and previously identified only on placenta and
colonic epithelium (27). The increased
glomerular expression of other cell surface receptors such as CD36 and CD163
suggests additional pathways that may be involved in the handling of glomerular
T cells infiltrate the kidney in lupus nephritis (28), and small numbers were clearly identified in most glomeruli by
immunohistochemistry (Table 1 and Figure
7B); however, relatively few T cell lineage
genes were identified by microarray analysis. This low number may reflect the strict
selection criteria used for clustering analysis and may be a false-negative finding.
However, the increased expression of transcripts in the pathway of MHC class II
antigen presentation suggests a glomerular potential to present peptide (auto-)
antigens to T cells. The presence in the glomerulus of a small cluster of genes
characterizing B cells and plasma cells correlated with numbers of glomerular T
cells, and in turn with high scores for interstitial inflammation and crescents
(Table 3) suggests that these lymphocytes are
present in concert with these processes, as has been observed in the NZB/W mouse
during nephritis (25). This raised the
possibility that cognate T cell–B cell interaction possibly driven by
autoantigen recognition occurs in the kidney and could enhance both glomerular
injury and the autoimmune response. The finding of increased expression of the
IFN-γ–induced IFI30 and of Eta-1 in lupus glomeruli together
with decreased expression of TGF-β point to an environment that would
favor Th1 cell differentiation. Eta-1 has been associated with the development of
Th1-mediated immunity (29). IFN-γ
has been identified as a major effector molecule that drives the development of
lupus in murine models of the disease (30).
While the role of the T cell in the nephritic glomerulus remains incompletely
documented in these microarray experiments, it was interesting to find a relative
accumulation of CD3+ cells in periglomerular interstitial
regions of the lupus biopsies that were studied by microarray (Figure 7B), raising the separate question of why T cells
preferentially accumulate in the interstitium.
Global glomerulosclerosis or fibrosis characterizes end-stage renal disease
regardless of specific etiology, and a clinically important issue in lupus nephritis
is to identify the individuals who will probably progress to renal fibrosis. The
observation that in different biopsies the expression of the fibrosis cluster
correlated with the presence of crescents and the hyaline matrix deposition of wire
loops (Table 3) suggests that these
transcripts indeed mediate certain elements of glomerulosclerosis. Interestingly,
although there was one instance of divergent glomerulus-to-glomerulus expression of
the fibrosis genes, in most biopsies, the pattern of fibrotic gene expression was
usually found in both glomeruli with morphological evidence of sclerosis and
glomeruli with minimal or no involvement at the morphological level. This suggests
that the early identification of this pattern in glomeruli with little
histopathological evidence of frank glomerulosclerosis or crescent formation may
denote an individual at high risk of progression to this response.
Transcripts for collagen I α2 and collagen VI α3 are
increased in lupus glomeruli as part of the fibrosis cluster. Both transcripts are
also increased in diabetic glomerulosclerosis and crescentic glomerulonephritis
through a pathway involving TGF-β1 (16, 20, 25). Intriguingly, in terms of the mechanism of fibrosis
in lupus, the present study suggests it does not occur through a
TGF-β1–mediated pathway, as lupus glomeruli exhibit a
striking decrease in expression of TGF-β1 and two of its receptor
proteins, as well as the TGF-β1–induced PAI-1 (15). Conversely, there are several genes that
could play a role in regulating the development of lupus glomerulosclerosis: MCP-1
is highly expressed in the fibrosis-related cluster and has previously been linked
to some forms of nephropathy (21), and the
increase in matrilysin (MMP7) is significant, as MMP7 promotes fibrosis in
interstitial pneumonitis (31).
Another member of the fibrosis cluster is RUNX1, encoding a hematopoietic
transcription factor that is emerging as a central transcriptional regulator in
autoimmune disease. The RUNX1 product interacts with PDCD1, located at chromosome
2q37 and encoding a transcript that regulates programmed cell death. PDCD1 has been
identified as a strong candidate gene for increasing susceptibility to SLE (32) through a single-nucleotide polymorphism
that alters a RUNX1 binding site. Recently, susceptibility to rheumatoid arthritis
(33) and to psoriasis (34) were also shown to be associated with genes that
contained altered binding sites for RUNX1 and that were encoded by loci located at
chromosomes 5q31 and 17q25, respectively.
One of the more striking features of lupus glomeruli was the pattern of high
expression of genes with promoters containing type I IFN response elements that was
found in approximately half of the biopsies and accounted for most of the genes in
the SG2 subgroup. The expression of these transcripts was highly concordant among
all glomeruli from the same biopsy, suggesting that an extraglomerular process may
regulate their presence in the kidney. Although the expression of genes with type I
IFN response elements in lupus glomeruli could be responsible for glomerular injury
in the SG2 group, their presence in these glomeruli was inversely correlated with
that of many genes in the fibrosis cluster and with the presence of cellular and
fibrous crescents, otherwise considered as histological elements indicating a
“high-risk” of lupus nephritis (35). This suggests that an event related to the
expression of these transcripts might either cause a milder form of renal injury or
be a protective response to glomerular injury. If protective, however, the presence
of fibrosis-related transcripts in a few of the SG2 glomeruli emphasizes that the
protection from glomerulosclerosis is not absolute.
In terms of the mechanism responsible for the coordinate induction of the genes
containing type I IFN response elements in glomeruli, there was no evidence of
elevated glomerular levels of IFN-α transcripts, although
IFN-α is elevated in peripheral blood of some individuals with SLE
(36, 37). Moreover, preliminary data demonstrate that the increased expression of
type I IFN–inducible transcripts by the SG2 group of lupus biopsies
(Figure 4) is restricted to the glomerulus, as
qRT-PCR analyses of ISG15 and MX1 in renal interstitial tissue was markedly lower
than in the corresponding glomerulus (data not shown). This also emphasizes the
technical importance of studying gene expression in individual renal compartments.
Recently it was demonstrated that type I IFN–inducible transcripts are
increased in peripheral blood leukocytes of some individuals with active lupus
(38, 39), and it is possible that this elevation could parallel the glomerular
increase in type I IFN–inducible transcripts observed in this study. The
present finding that activated NK cells express all of the type I IFN response
element–containing transcripts that were identified in lupus glomeruli
suggests one additional interpretation of this “signature”
in the kidney: that the entrance of activated NK cells could be responsible for this
phenotype, mediating either an injurious or a protective effect. This is supported
by the finding that a few genes not containing type I IFN response elements were
also included in this cluster and were increased in NK cells.
Perhaps also related to this is the observation that some type I
IFN–inducible transcripts identified in this study were located in
genomic regions associated with the lupus susceptibility. MNDA is
found in the lupus susceptibility region at 1q22 (40), and ifi202, a murine homolog of MNDA
that is encoded by a region syntenic to 1q22, is an interesting gene indicating
susceptibility to lupus in the mouse (41). A
second IFN-inducible gene, SP100, is located within another region
associated with lupus susceptibility in the human genome, at 2q36-37 (40). This aspect of the action of some type I
IFN–inducible transcripts may be relevant to the development of
autoimmune phenomena that occasionally appear during the use of IFN-α in
the treatment of chronic viral hepatitis and certain malignancies (42). However, until the significance of the expression of
type I IFN–inducible genes in lupus is better understood, some caution
may be warranted if considering blockade of IFN-α induction as a
Clinical samples. The biopsies were from individuals with SLE who had newly identified renal
involvement leading to diagnostic renal biopsy and for which there was abundant
residual “archival” frozen tissue (Table 2). Controls were kidney biopsies that appeared
normal by histopathological examination. These were either from kidneys in which
there was minimal isolated proteinuria or hematuria but no pathological
abnormalities were identified by renal biopsy, or from uninvolved portions of a
kidney at the time of nephrectomy for tumor. One study population (called B13)
included 11 individuals with lupus nephritis (17 independently processed
glomeruli) and 2 control individuals (3 independently processed glomeruli). A
second sample set (called B12) included eight glomeruli isolated from three SLE
biopsies and three control glomeruli from two different control individuals.
Some SLE biopsies, represented by different glomeruli, were studied in both
sample sets. The two sample sets were processed at different times and were
analyzed on slightly different versions of the cDNA microarray, as indicated in
Methods, cDNA microarray. All biopsies had been stored at
–80°C from several months to more than a year before
being processed for microarray analyses, and only those biopsies that had been
frozen within approximately 1 hour of collection were used for laser capture.
Standard histological, immunofluorescence, and electron microscopy studies were
used to classify the biopsies (Table 1)
(4, 6). Quantification of infiltrating T cells (CD3) and macrophages (CD68)
was done using immunoperoxidase staining of formalin-fixed tissue. For gene
expression analyses, if more than one glomerulus was analyzed from the biopsy,
each glomerulus was isolated by laser-capture microscopy, processed, and
analyzed individually by microarray hybridization. The use of
“archival” renal biopsies and clinical data reported in
this study were approved by the Columbia University Institutional Review Board
Laser-capture microscopy and RNA/cDNA sample preparation. For each glomerular sample, four to six sections 7 μm in thickness of
a frozen biopsy were used for laser capture of consecutive levels of the same
glomerulus (10, 43) to minimize sampling effect within a glomerulus.
Approximately 1–5 ng of RNA were extracted from each laser-captured
glomerular sample. After two rounds of T7 promoter–based RNA
amplification, each sample typically provided a final yield of
50–100 μg of amplified mRNA (10, 24).
Because of the potential for transcript truncation or degradation, no sample was
processed further unless the adequacy of each amplification was verified by
quantitative PCR amplification of aliquots of RNA removed prior to and after
each step in the amplification process using primers specific for proximal and
distal exons of a ubiquitous eukaryotic elongation factor (EEF1A1) as described
(24). The overall quality of the
amplified RNA was also assessed by agarose gel electrophoresis (data not
cDNA microarray. The human cDNA microarrays were spotted onto Corning GAPS slides (Corning Life
Sciences, Acton, Massachusetts, USA) using an Amersham Biosciences Generation
III spotter (Amersham Biosciences, Piscataway, New Jersey, USA). The B12 array
contained 3,602 genes and the B13 array had the same 3,602 genes plus an
additional 428 genes. Each clone was spotted in duplicate on the array. Of the
clones on the array, 59% were IMAGE clones purchased from Research
Genetics (Invitrogen, Carlsbad, California, USA). The microarrays contained a
set of expressed human genes that were chosen based on the availability of human
cDNA clones at that time rather than their functional characteristics. The major
functional families of cDNAs on the microarray included receptor molecules
(25% of clones), ion channels (6.5% of clones),
immunoglobulin family (1% of clones), cytokines and chemokines
(3.3% of clones), enzymes (18% of clones), adhesion
molecules (4% of clones), transporters (5% of clones),
signal transduction molecules (5% of clones), DNA-binding molecules
(2.6% of clones), and chaperones (3.2% of clones). The
indocarbocyanine-labeled cDNA probe preparation, hybridization, and subsequent
washes of the arrays were performed according to the methods described by Luo et
al. (10). The arrays were scanned in a
ScanArray 4000 (Perkin Elmer Life Sciences, Boston, Massachusetts, USA).
Quantification was done using Imagene (Biodiscovery, Marina del Rey, California,
Quantitative PCR analysis. Total RNA was extracted from two to four laser-captured glomeruli isolated as
described above using the Qiagen Micro RNA kit (Qiagen Inc., Valencia,
California, USA). Total RNA was converted to cDNA without prior amplification of
RNA using the First Strand Synthesis System (Invitrogen) and an oligodT primer.
The cDNA (0.4 μl for each reaction) was subjected to SYBR
green–based qRT-PCR using a Smart Cycler (Cepheid, Sunnyvale,
California, USA) according to the manufacturer’s instructions. The
target CTSC, ISG15, and MX1 genes were amplified using the following primers:
for CTSC, CAGACCCCAATCCTAAGCCCTCAG (5′) and
GCCATAGCCCACAAGCAGAACAGC (3′); for ISG15, GGCGGGCAACGAATTCCAGGTGT
(5′) and CTCCCCGCAGGCGCAGATTCA (3′); and for MX1,
TGCTGCATCCCACCCTCTATTACT (5’) and GGCGATGGCATTCTGGGCTTTAT
(3′). Amplification of the reference gene EEF1A1 using primers
CATGCAAGTTTGCTGAGCTG (5′) and GATGCATTGTTATCATTAACC (3′)
and of a negative control was included in each experiment in separate reactions.
Because the quantity of the template was limited, DNAse treatment was not
performed and instead the two primers were designed so that the target cDNA
spanned at least one intron in the genomic sequence. The qRT-PCR reaction was
performed in the presence of 20 mM Tris-HCl, pH 8.4, 50 mM KCl, 3 mM
MgCl2, 0.2% Tween-20, 0.15 M Trehalose, 0.2 mg/ml BSA,
0.4 mM dNTPs, 0.4∞ SYBR green, 0.1 U Platinum Taq Polymerase
(Invitrogen), and 12.5 pmol of each primer per reaction in total reaction volume
of 25 μl. The quantity of input cDNA was titrated to determine the
concentration range at which the efficiencies of target and reference were
approximately equivalent. The specificity of the resulting amplifications were
confirmed by a melting curve in each experiment, and experiments were considered
valid only if the melting curve showed a single peak at the expected melting
temperature for each PCR product. The comparative threshold (CT) cycle method
was used to calculate the amplification of the genes relative to the reference
control gene EEF1A1. Because the microarray clustering analyses were performed
using the normalized value of the logarithm of the intensity, the analysis of
the kinetic PCR quantification was made using Δ CT values rather
than “fold changes,” which would involve exponentiation.
The ΔΔ CT value for each data point was calculated by
using the negative of the value obtained by first subtracting the CT for the
reference gene from the target and then subtracting the Δ CT
calibrator value, or ΔΔ CT =
–([CT target – CT reference]
– CT target calibrator), where CT target calibrator was the
numerically greatest observed CT value for a given target across all
experiments. Four to nine replicates were determined for each sample. The
correlation between the means of the determinations for each target by the CT
cycle method versus the mean logarithm of the intensities in the microarray
experiment for the same target was assessed by calculation of the nonparametric
Spearman rho coefficient, and the linear regression of the scatter plot was
calculated using SPSS (SPSS, Chicago, Illinois, USA).
Microarray data normalization. The cDNA synthesized from each control or SLE glomerular RNA sample was
hybridized to two microarray slides that each contained a cDNA array spotted in
duplicate, yielding a quadruplicate data set for each sample. Outliers were
automatically excluded prior to normalization (44). For analysis, the intensities from each data set were scaled at
the 75th percentile (the 75th percentile value of each data set was set to 100)
followed by log transformation in base 2, and then were normalized in a two-step
procedure (44). The first normalization
step was performed across arrays within the same RNA sample. The mean intensity
of the quadruplicates was then determined, yielding a matrix of the expression
values for 4,030 genes and 31 samples (25 SLE glomeruli and 6 control
glomeruli). A second normalization was performed across control and SLE samples
within each of the two experimental sets, B12 and B13. The resulting, unanalyzed
data file is in Supplemental Table 2,
which is subdivided according to the two data sets.
Microarray analyses of a peripheral blood reference panel. A reference set of cells was isolated from the peripheral blood of one to five
normal donors, depending on the cell type. Monocytes, NK cells, B cells, and T
cells were isolated from Ficoll Hypaque–purified PBMC samples using
CD14-, CD56-, CD19- and CD4/CD8-coupled Miltenyi beads, respectively (Miltenyi
Biotec, Cologne, Germany). Basophils were isolated from PBMC samples using the
Miltenyi Basophil Isolation Kit 4. Neutrophils and eosinophils were isolated
from non-PBMC samples using CD16- and CD15-coupled Miltenyi beads, respectively.
Activated CD14 cells or neutrophils were stimulated in overnight cultures in the
presence of LPS or PMA, as indicated in Figure 3. Immature dendritic cells were obtained after a 7-day culture of CD14
cells in the presence of IL-4 and GM-CSF, and dendritic cells were isolated
after an additional 24-hour incubation in the presence of TNF-α.
Activated NK cells were obtained after an overnight culture of CD56 cells in the
presence of LPS. Activated B cells were prepared by incubation of CD19 cells
overnight in CD40-coated wells or in the presence of PMA. CD4 T cells and CD8 T
cells were activated by overnight culture in the presence of OKT3 (mouse
monoclonal antibody to human CD3) and CD28. RNA extraction, cDNA probe
preparation, and cDNA microarray hybridization were done as described below,
with the exception that RNA was not amplified prior to cDNA synthesis.
Selection of genes that differ in expression between SLE glomeruli and
controls. Transcripts unlikely to contribute significantly to the differential gene
expression profiles of the two groups of glomeruli were filtered out first by
determination of the difference in gene expression between lupus glomeruli and
controls on the final normalized data for each gene on the microarray using a
Welch modified t test (S-PLUS 2000; Insightful, Seattle,
Washington, USA). A Benjamini and Hochberg (45) stepwise procedure was used to control the false discovery rate
below 0.05. Genes with adjusted P values greater than 0.05 were
considered not to have significant differences between SLE and controls and were
removed from further analysis.
The second selection step was based on calculation of the log ratio of expression
between experiment and control for each gene, using for the control value the
expression of the gene averaged across all controls (C-avg.). As the expression
values were already “log transformed,” this ratio was
obtained by subtraction of C-avg. from the log expression value of the gene in
each sample. In this selection step, we included only genes that had a change in
this log ratio of at least a twofold in one third or more of the SLE glomeruli,
resulting in 177 genes that were selected for subsequent cluster analysis.
Cluster analysis. The log ratios for the expression of each of the 177 genes, as defined above,
were clustered in the OmniViz version 3.0 software (OmniViz Inc.). An
agglomerative hierarchical cluster analysis was performed using average linkage
and a correlation metric unless otherwise stated.
Subgrouping of SLE samples and selection of subgroup-specific genes. The variance of each gene among the 11 different SLE biopsies in set B13 was
estimated individually by a nested ANOVA model (S-PLUS 2000; Insightful); the
structure of the data was biopsy/glomerular sample. Using this model, genes with
high biopsy-to-biopsy variance were selected (P ≤
0.05) and the ratio of the expression in each SLE glomerulus to the mean
expression in the controls was calculated for each gene and was analyzed by
hierarchical clustering. This identified the two biopsy subgroups designated SG1
Differential gene expression between SG1 and SG2 was then determined for each
gene by nested ANOVA using the structure SLE subgroup/biopsy/glomerular sample.
The Benjamini and Hochberg procedure was further applied to keep the false
discovery rate below 0.05 (45). Genes
were selected based on adjusted P values of 0.05 or less and a
ratio of twofold or more between the mean of the expression in SG1 versus SG2.
For the selected genes, the ratio of their expression in each SLE glomerulus
over the mean of the controls was analyzed by hierarchical clustering using
average linkage and a Euclidean correlation metric.
Expression of individual gene clusters in each glomerular sample and the
correlation to histopathological markers of disease. To obtain an overall measure of the expression of a gene cluster by individual
glomeruli, we calculated the mean value of the intensity scores (log ratio of
expression in a lupus sample over geometric mean of controls) for all genes in
the cluster. A value of 0.6 or more (equal to a change of approximately 1.5-fold
over controls) was used to indicate expression of a cluster by a glomerular
sample. The semiquantitative morphological variables (immunopathology and
markers of activity and chronicity) characterizing each glomerular sample were
then compared between the group of glomeruli expressing a certain gene cluster
and the group not expressing the gene cluster using the Wilcoxon rank sum test
View Supplemental table 1
View Supplemental table 2
Ann Moriarty, Didier Leturcq, and Juli DeGraw (Johnson & Johnson
Pharmaceutical Research and Development) are gratefully acknowledged for allowing us
to use data from their unpublished microarray analyses of a set of peripheral blood
cell cultures. We also thank Valeria Steshenko for performing the qRT-PCR analyses;
Lin Luo and Anton Bittner for excellent technical assistance; and Fredrik Kamme and
Xiao-Jun Ma for valuable discussions. This study was in part supported by grants
from the National Institutes of Health (P30 HD34611, K01 DK002853, U19 AI046132, and
Mark Erlander’s present address is: Arcturus Applied Genomics,
Carlsbad, California, USA.
Nonstandard abbreviations used: comparative threshold
(CT); Fc receptor (FcR); New Zealand Black/White (NZB/W);
quantitative real-time PCR (qRT-PCR); systemic lupus erythematosus (SLE).
Conflict of interest: The authors have declared that no conflict of
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