Characterization of heterogeneity in the molecular pathogenesis of
lupus nephritis from transcriptional profiles of laser-captured
glomeruli ([original](https://doi.org/10.1172%2FJCI200419139)) ([raw](?raw))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, ∞20.
Gene expression profiles of lupus glomeruli. (A andB) 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 ofA 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. InA, 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 inA. 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 group.
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 response elements.
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 Figure2B 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 Figure 2A.
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 cell genes.
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 shown.
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 (Table3). 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 andB) 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). Magnification, ∞20.
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.