1 Division of Nephrology, Department of Medicine, and 2 CWRU Cancer Center, School of Medicine, Case Western Reserve University, University Hospitals of Cleveland, Cleveland, Ohio 44106
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ABSTRACT |
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Control of mesangial cell growth and matrix accumulation is critical for normal development of the glomerular tuft and progression of glomerular injury, but the genes that control mesangial cell growth are not well understood. We used high-density oligonucleotide microarrays to analyze gene expression in well-differentiated human mesangial cells treated with serum to stimulate proliferation. Parallel measurement of >12,000 genes and expressed sequence tags identified 5,806 mRNA transcripts in quiescent, unstimulated cells and 609 genes significantly induced or repressed by serum. Functional classification of serum-regulated genes revealed many genes not directly related to cell cycle progression that, instead, might control renal hemodynamics and glomerular filtration or cause tissue injury, leukocyte exudation, matrix accumulation, and fibrosis. Hierarchical cluster analysis defined sets of coregulated genes with similar functions and identified networks of proinflammatory genes with similar expression patterns. Pathway analysis of the gene expression profile suggested an autocrine role in mesangial cell proliferation for three growth factors in the epidermal growth factor (EGF) family: heparin-binding EGF-like growth factor, amphiregulin, and epiregulin. A functional role for EGF receptor (EGFR) activation was confirmed by blocking serum-induced proliferation with an EGFR-selective kinase inhibitor and a specific EGFR-neutralizing antibody. Taken together, these results suggest a role for EGFR signaling in control of mesangial cell growth in response to serum.
glomerulus; growth; glomerulosclerosis; heparin-binding epidermal growth factor-like growth factor; microarray
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INTRODUCTION |
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GLOMERULAR MESANGIAL CELLS and the associated mesangial matrix are essential for normal structure and function of the glomerular capillaries (8, 10, 13, 16, 21). Mesangial cells support the capillary loops and preserve glomerular architecture in the face of the high hydraulic pressure necessary for glomerular ultrafiltration (14). Glomerular hemodynamics are modulated by mesangial production of vasoactive mediators and by control of the ultrafiltration coefficient. In rats with mesangiolysis, glomerular hemodynamics in response to volume loading and angiotensin II are altered, supporting a specific role for mesangial cell control of the ultrafiltration coefficient and efferent arteriolar tone (2). Mesangial cells also possess important phagocytic functions that remove macromolecules and immune complexes that enter the mesangial matrix through the fenestrated glomerular endothelium.
The phenotype of mesangial cells is highly plastic, and control of
mesangial cell growth is critical in glomerular physiology and
pathophysiology. Mesangial cells are recruited to the glomerulus at a
late stage in metanephrogenesis. The mechanisms governing mesangial
cell growth in development are unclear, but analysis of glomerular
phenotype in null mice and chimeras demonstrates a role for
platelet-derived growth factor (PDGF) B and the PDGF receptor- in
growth and formation of normal mesangium (19). In the
adult kidney, mesangial cells are largely quiescent, with a renewal
rate of <1% (10, 13, 21). However, glomerular injury can
alter the phenotype of mesangial cells, resulting in hypertrophy,
hyperplasia, and/or expansion of the mesangial matrix. This
injury-dependent phenotypic switch of mesangial cells contributes to
glomerulosclerosis and is present in different forms of renal disease,
including diabetic nephropathy, lupus nephritis, and IgA nephropathy.
Recent studies in experimental models of renal disease and in humans
suggest that attenuating mesangial hypercellularity and matrix
accumulation preserves renal function by slowing glomerulosclerosis (7, 8, 10).
Although autocrine or paracrine mediators of mesangial cell growth have been identified, we know little about specific genes and regulatory networks that underlie control of mesangial cells and matrix accumulation. To explore genes and regulatory pathways that control mesangial cell growth, we used high-density oligonucleotide microarrays (20) to simultaneously monitor expression of >12,000 genes in human mesangial cells treated with serum to stimulate proliferation. Analysis of the gene expression profile suggested an unexpected role for signaling by the epidermal growth factor (EGF) receptor (EGFR) pathway, which was confirmed by inhibiting EGFR activity. These experiments point to the utility of functional genomic approaches for providing data to model gene regulatory pathways in mesangial cells.
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MATERIALS AND METHODS |
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Cell culture and experimental design. Mesangial cell strains (passages 4-11) from human kidneys were freshly isolated and characterized as previously reported (11, 25). Briefly, human kidney cortex was obtained from the National Cancer Institute Cooperative Human Tissue Network at Case Western Reserve University after surgical excision for associated localized neoplasm. All kidney tissue was distant from the neoplasm and macroscopically normal. Separate mesangial cell cultures were isolated from two donors. Cells were maintained in RPMI 1640 medium supplemented with 17% fetal bovine serum (FBS), penicillin (100 U/ml), streptomycin (100 µg/ml), selenite (5 ng/ml), and insulin and transferrin (5 µg/ml each). Cells were characterized by phase-contrast microscopy and by immunostaining for intermediate filaments and surface antigens, as described by Schultz et al. (25). Briefly, cells were positive for desmin, vimentin, and myosin but did not stain for factor VIII, keratin, or common leukocyte antigen. To mimic inflammatory stimuli, semiconfluent cultures were made quiescent for 24 h in 0.5% FBS and then stimulated with 20% FBS for 0.5, 1, 2, 6, 16, and 24 h. Total RNA was isolated, and mRNA transcripts were analyzed as described below. Each experiment was performed in duplicate in each of the two cell lines, and the RNA was pooled.
Microarray analysis of steady-state mRNA expression. Target RNA was labeled and hybridized to high-density oligodeoxynucleotide microarrays (Affymetrix, Santa Clara, CA). Briefly, total RNA was isolated (RNeasy, Qiagen, Valencia, CA), and 5 µg were used to synthesize cDNA with a T7-(dT)24 primer (Genset, La Jolla, CA) and RT Superscript II (GIBCO BRL, Rockville, MD) for 1 h at 42°C. After second-strand synthesis and removal of contaminating RNA, cRNA was prepared from the cDNA by in vitro transcription with biotinylated UTP and CTP (Enzo Diagnostics, Farmingdale, NY). The biotin-labeled cRNA (15 µg) was then fragmented and hybridized to Test 2 arrays to assess sample quality and then to Human Genome U95A microarrays for 16 h at 45°C in a buffer containing 100 mM MES, pH 6.6, 1.0 M sodium salts, 0.01% Tween 20, herring sperm DNA (0.1 mg/ml), and acetylated bovine serum albumin (0.5 mg/ml). The U95A microarray represents 12,625 gene sequences derived from expressed sequence tag (EST) clusters in Build 95 of Unigene. Of these sequences, 10,929 are annotated genes (GenBank or TIGR), 1,629 are unannotated ESTs, and the remainder are duplicate or control probe sets. After stringent washing in a microfluidics station, bound cRNA was stained with R-phycoerythrin-streptavidin (Molecular Probes, Eugene, OR) and scanned before and after antibody amplification. Fluorescence intensities were analyzed with a laser confocal scanner (Hewlett Packard). Image output files were inspected for hybridization artifacts, and, on the basis of fluorescence intensity differences between perfect-match and mismatch probes, absolute mRNA detection call (present, marginal, or absent) was determined using a P value calculated from a Wilcoxon signed-rank test (Microarray Suite 5.0, Affymetrix). Default parameters optimized by the manufacturer for this microarray chip were used for all detection and change analysis.
Analysis of mRNA expression data. The transcript signal, which indicates mRNA transcript abundance, was used to determine whether transcript levels were altered in serum-stimulated cells at different time points. Scanned images were globally scaled to a target intensity of 1,500 to facilitate comparison of transcript levels from different time points. Significant changes in transcript level were determined using the Wilcoxon signed-rank test to compare individual probe pair data for each transcript between time 0 and serum-stimulated arrays. From the resultant P values, change calls (increase, decrease, or no change) were assigned to each transcript at every time point. To increase the likelihood of identifying significantly changed genes, the data set was filtered to remove transcripts that increased or decreased less than twofold. Expression analysis files were transferred to Microsoft Access and linked to Internet genome databases (e.g., GenBank, LocusLink, and SOURCE). To help identify structural patterns of gene expression, genes significantly induced or repressed by serum were organized into groups using pairwise average-linkage cluster analysis, a form of hierarchical clustering previously used to identify coexpressed genes in yeast and human fibroblast microarray experiments (3, 5, 12). We used the hierarchical clustering algorithm and visualization software package (Cluster and TreeView) developed by Eisen and co-workers (5) with the Pearson correlation coefficient as the metric of similarity. GenMAPP, a computer application available from the Conklin laboratory at the University of California, San Francisco (www.GenMAPP.org), was used to visualize gene expression data on maps representing biological pathways. Data reduction by principal components analysis, or singular value decomposition, was performed according to standard routines implemented in the SPSS statistical software package and confirmed using Cluster (5).
Estimation of mRNA levels by real-time RT-PCR. Isolation of total RNA after FBS stimulation and synthesis of cDNA was carried out as described above for microarray analysis. cDNA transcribed from equivalent amounts of RNA was used for quantification of mRNAs by RT-PCR as previously described with minor modification (22). The reaction mixture (total volume of 20 µl) contained 2 µl of FastStart DNA Master Syber green I (Roche Molecular Biochemicals), 3 mM MgCl2, and 2 µl of PCR primer sets for human interleukin (IL)-6, IL-8, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH; LC Search, Heidelberg, Germany). RT-PCR was carried out for 40 cycles as follows: 5 s at 95°C, 5 s at 68°C, and 16 s at 72°C preceded by 10 min of incubation at 95°C. Using agarose gel electrophoresis of the RT-PCR products, we confirmed that only one band of the predicted molecular weight was present. To construct a standard curve, known concentrations of cDNA specific for IL-6, IL-8, and GAPDH were run in parallel to the experimental samples. A melting curve recorded at the end of reaction was used for correction of the amplification curve. Amounts of mRNA in FBS-stimulated samples were calculated from the standard curve plot of the crossing point in the log-linear range vs. the logarithm of the copy number. Amounts of the mRNA transcripts are presented relative to the amount in the untreated cells.
Western blotting of heparin-binding EGF-like growth factor protein in FBS-stimulated mesangial cells. Western blotting was carried out as previously described with minor modifications (11). After addition of 20% FBS or PDGF BB (10 ng/ml; R & D, Minneapolis, MN), cells in 100-mm dishes were washed once in Dulbecco's PBS and scraped into 1 ml of sample lysis buffer. The lysate was vortexed and boiled for 5 min, aliquots were resolved on 4-20% SDS-PAGE gradient gels, and proteins were transferred to 0.2-µm nitrocellulose filters. Transferred proteins were stained with Ponseau S to confirm equal protein loading, and the filter was blocked overnight at 4°C. The membrane was probed with a 1:2,000 dilution of an affinity-purified goat anti-human heparin-binding EGF-like growth factor (HB-EGF) antiserum (R & D). After extensive washing, the appropriate peroxidase-labeled secondary antibody in blocking buffer (1:10,000) was added, and the proteins were detected by chemiluminescence. Typical exposure times were 30-60 s.
Measurements of mesangial cell proliferation. Cells (5 × 103) were plated into 12-well plates and made quiescent in 0.5% FBS for 24 h before stimulation with 20% FBS or PDGF BB (10 ng/ml). At 24 and 48 h after stimulation, the number of cells in trypsinized monolayers was quantified using a hemocytometer. In some experiments, serum-starved cells were pretreated for 1 h before addition of FBS or PDGF with 500 nM AG-1478 (Calbiochem, La Jolla, CA) in 0.001% DMSO, a selective inhibitor of the EGFR kinase (18), or with a monoclonal neutralizing antibody (10 µg/ml; clone LA1, UpState Biotechnology, Lake Placid, NY) against an extracellular epitope of EGFR (26). Mesangial cells were also pretreated with DMSO or an eqimolar concentration of mouse IgG1 (Sigma, St. Louis, MO) to control for nonspecific effects. Each experiment was replicated three times, and statistical significance was calculated using an unpaired t-test.
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RESULTS |
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Identification of genes regulated by serum.
In normal glomeruli, mesangial cells remain largely quiescent
(G0) until stimulated by inflammatory mediators. In our
experiments, cultured mesangial cells deprived of serum to mimic
quiescence expressed 5,806 of 12,558 mRNA transcripts represented on
the U95A chip (46.2%). (The entire G0 gene set is
available as a supplement at www.cwru.edu/med/simonson.) The
mRNA transcripts present in G0 cells represented annotated,
characterized genes and many genes identified only by ESTs. To assess
reproducibility, we hybridized two independently prepared RNA
preparations from different quiescent cell strains. Of the 12,558 probe
sets, each representing a specific gene, 4.3% showed a twofold or
greater difference between replicate measurements. The reproducibility
of these results parallels previously reported data using high-density
microarrays to measure mRNA levels in yeast (29). After
stimulation of G0 cells with serum, 644 mRNA transcripts
were increased or decreased over the 24-h time course. (A complete list
of genes induced or repressed by serum is available at
www.cwru.edu/med/simonson.) A principal components analysis showed that
91.4% of the variance over the entire time course can be explained by
reducing the expression data to three components: factor 1 of 0.5, 1, and 2 h; factor 2 of 6 h; and factor 3 of 16 and 24 h. A representative list of
serum-regulated genes classified according to the biological process
and molecular function nomenclature of the Gene Ontology Consortium
(1) is presented in Table 1.
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Analysis of regulatory networks in serum-stimulated mesangial
cells.
To gain insight into pathways that control mesangial cell growth, we
used hierarchical cluster analysis to identify structural patterns of
gene expression (5). The rationale for this approach is
that clusters might represent groups of genes regulated by common or
closely related pathways, an approach validated previously in yeast
(3, 4, 30). Figure 2
illustrates clusters that emerge from a
hierarchical analysis of the 609 genes significantly induced or
repressed by serum (644 total transcripts when duplicated probe sets
are included). In this pictorial representation, increasing intensity
of red indicates the degree of mRNA induction, and the intensity of
green denotes repression. Among the interesting clusters to emerge was
one containing many genes known to function in inflammation, which is
mapped and enlarged (IG in Fig. 2). This cluster also contained genes
involved in transcription and cell signaling. Among these were the
transcription factors fosB, junB, Egr2 and Egr3,
and Atf3 and loci of the nuclear receptor subfamily. Genes involved in cell signaling included the cytokines IL-6, IL-8, and
IL-11; three members of the EGF family, amphiregulin, epiregulin, and
HB-EGF-like factor; vascular endothelial growth factor;
dual-specificity phosphatases 1 and 5; Ras-related associated with
diabetes; and a novel member of the Akt family,
serum/glucocorticoid-regulated kinase (SGK). A recent study
by Lang et al. (15) showed elevated mesangial cell levels
of SGK in biopsies from patients with diabetic nephropathy,
which supports the utility of our experimental approach to finding gene
networks in activated mesangial cells. Many of these genes have not
previously been associated with mesangial cell activation and might
represent loci that control mesangial hypercellularity and matrix
accumulation.
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Role of EGFR signaling in mesangial cell proliferation.
Insight into signals that control mesangial cell growth might be gained
by analyzing changes in expression for genes that participate in
specific signal transduction pathways. This approach was recently used
to identify regulatory mechanisms in a mouse model of cardiomyopathy
(24). We initially focused on signaling by the EGFR
family, because our gene expression profile showed that growth factors
binding to the EGFR were markedly induced by serum. Figure
3 shows the results of this analysis
using GenMAPP. Serum stimulated expression of three EGF-family growth
factors that bind to EGFR: HB-EGF (8.6-fold maximum induction at 2 h), amphiregulin (6.5-fold at 6 h), and epiregulin (4.9-fold at
6 h). We confirmed induction of HB-EGF by analyzing HB-EGF protein levels by Western blotting in quiescent cells stimulated with serum
(Fig. 4A) or PDGF BB (Fig.
4B). At 2-8 h after serum stimulation, HB-EGF protein
levels increased; after 24 h, HB-EGF protein decreased but
remained elevated over time 0. Serum increased expression of
a 25-kDa transmembrane HB-EGF and lesser amounts of 16- to 19-kDa
sHB-EGF, which represents a fraction of the proteolytically processed
and secreted form of HB-EGF that remains with the cell monolayer
(9). PDGF, a potent mesangial mitogen, also increased HB-EGF protein levels, confirming that a defined mitogen can also induce HB-EGF expression (Fig. 4B). Genes encoding EGF and
transforming growth factor-, which also bind to EGFR, were expressed
at low levels in quiescent mesangial cells and were not regulated by serum (Fig. 3). EGFR (Her1), Her2, and
Her3 were expressed in mesangial cells but were not
regulated (Fig. 3), suggesting that activation of EGF signaling occurs
at the level of ligand induction and not at the receptor level.
Interestingly, few genes encoding downstream effectors of EGFR
signaling were regulated by serum (Fig. 3). An important exception was
mitogen-activated protein kinase (MAPK) phosphatase 1, which
downregulates agonist-stimulated MAPK. Transcription factors or distal
effectors of EGFR signaling were induced, including c-myc,
the forkhead transcription factor (FKHR), c-fos,
c-jun, and the antiapoptotic gene Bcl2.
Similar pathways can be constructed for other signaling pathways in
activated mesangial cells.
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DISCUSSION |
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An important challenge in renal cell biology is to understand gene regulatory pathways that control mesangial cell growth. Using oligonucleotide microarrays, we mapped a gene expression profile in quiescent and serum-stimulated human mesangial cells, analyzed potential regulatory networks, and identified unknown human genes regulated by serum.
A surprising number of genes regulated by serum were not directly related to cell cycle progression and, instead, seem likely to regulate renal hemodynamics and glomerular filtration or cause tissue injury, leukocyte exudation, matrix accumulation, and fibrosis. Shortly after the induction of immediate-early transcription factors (e.g., c-fos, Egr, and Atf3), many growth factor, cytokine, and chemokine genes were strongly induced at 0.5-1.0 h, including IL-8, activin A, and HB-EGF. At 2 h, another wave of genes was markedly induced, including loci for amphiregulin, IL-6, parathyroid-like hormone, and cyclooxygenase-2. At 6 h after serum stimulation, genes involved in leukocyte exudation were induced, including intercellular adhesion molecule-1 and monocyte chemotactic protein-1. Two other proinflammatory genes were induced at 6 h: the gene for Kit ligand, a pleiotropic factor that controls cell migration, and leukemia inhibitory factor, a cytokine that stimulates macrophage differentiation and cell growth. Also notable at 6 h was induction of loci for the extracellular proteases granzyme K, serine proteases 1 and 4, and two genes involved in thrombosis, coagulation factor III and thrombomodulin. Regarding mechanisms of mesangial matrix accumulation, we observed gene induction of tissue inhibitor of matrix metalloprotease 3 (TIMP3), consistent with the evolving paradigm that glomerulosclerosis involves impaired degradation of matrix proteins (8).
After 6 h of serum stimulation, the gene expression profile became
increasingly complex and involved similar numbers of induced and
repressed genes. The Fc receptor locus was markedly repressed by serum,
which could help explain impaired clearance of immune complexes in
immune-mediated glomerular injury. The prostacylin synthase locus was
repressed (12.2-fold) at 16-24 h, which might have hemodynamic
consequences given the vasodilatory action of prostacyclin. Several
genes involved in cell-matrix and cell-cell interactions were induced
at 16-24 h, including the integrin-
2 loci. Although
some of the genes described above have been previously associated with
mesangial cell activation (10, 13, 21), most represent
potential new targets for understanding control of mesangial cell
growth. Moreover, we identified novel human genes (i.e., represented
only by ESTs) induced or repressed by serum, which might include
candidates for regulation of mesangial hypercellularity and matrix accumulation.
An unexpected finding from mRNA expression profiling in mesangial cells
is that serum stimulated genes that activate the EGF signaling pathway.
Previous experiments have emphasized the importance of several
autocrine/paracrine signaling mechanisms in mesangial cell activation,
including PDGF, transforming growth factor-, fibroblast growth
factor, angiotensin II, and endothelin-1 (8, 10, 13). We
confirmed that genes encoding these factors were indeed induced by
serum, but genes encoding HB-EGF, amphiregulin, and epiregulin were
induced to a far greater degree and generally displayed a more rapid
time course of induction. Serum also increased the level of HB-EGF
protein, as did a defined mitogen, PDGF. These EGF-family ligands bind
to EGFR (ERBB1), which was not significantly induced by
serum. Our experiments with a selective EGFR kinase inhibitor and a
neutralizing antibody support a role for EGFR signaling in serum- and
PDGF-stimulated proliferation. From gene profiling alone, it is
difficult to predict the precise role of EGFR signaling in mesangial
cell proliferation in vivo; however, glomerular HB-EGF protein is
elevated in animal models of diabetic nephropathy, antiglomerular
basement membrane nephritis, and mesangial proliferative nephritis
(6, 17, 23, 27). A dominant-negative mutant of EGFR in
proximal tubular cells attenuates tubulointerstitial fibrosis in an
ablative mouse model of renal disease (28), but the role
of EGFR signaling in mesangial cell growth in vivo remains to be
tested. We speculate that induction of EGF-like growth factors and the
signaling cascades they propagate might contribute by autocrine or
paracrine mechanisms to mesangial hypercellularity and the glomerular
response to injury.
Conclusions drawn from our study are limited in part by the fact that for some genes, changes in steady-state mRNA do not reflect changes in protein activity. For example, serum stimulates MAPK activity, but these changes are not detected by gene expression profiling. Thus a more complete understanding of mesangial hypercellularity and matrix accumulation requires expression profiling in conjunction with a proteomic assessment. In addition, as with genome biology in general (4, 30), we are limited by the need for additional computational and theoretical tools to understand the biological significance of mesangial gene expression profiles. However, the results of our study suggest testable hypotheses regarding pathways of gene expression that control mesangial hypercellularity and matrix accumulation.
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ACKNOWLEDGEMENTS |
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We thank Dr. Michael Eisen for providing the Cluster software.
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FOOTNOTES |
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This work was supported by National Institute of Diabetes and Digestive and Kidney Diseases Grant DK-46939.
Address for reprint requests and other correspondence: M. S. Simonson, Div. of Nephrology, Dept. of Medicine, Biomedical Research Bldg., Rm. 427, Case Western Reserve Univ., 2109 Adelbert Rd., Cleveland, OH 44106 (E-mail: mss5{at}po.cwru.edu).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
June 18, 2002;10.1152/ajprenal.00103.2002
Received 15 March 2002; accepted in final form 10 June 2002.
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