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Gene Expression Profiling in Glomeruli From Human KidneysWith Diabetic Nephropathy

Hans J. Baelde, BSc, Michael Eikmans, PhD, Peter P. Doran, PhD, David W.P. Lappin, MD, PhD,Emile de Heer, PhD, and Jan A. Bruijn, MD, PhD

Background: Diabetic nephropathy (DN) is a frequent complication in patients with diabetes mellitus. To findmproved intervention strategies in this disease, it is necessary to investigate the molecular mechanisms involved.o obtain more insight into processes that lead to DN, messenger RNA expression profiles of diabetic glomeruli andlomeruli from healthy individuals were compared. Methods: Two morphologically normal kidneys and 2 kidneysrom patients with DN were used for the study. Glomerular RNA was hybridized in duplicate on Human Genome95Av2 Arrays (Affymetrix, Santa Clara, CA). Several transcripts were tested further in independent patient groupsnd at the protein level by immunohistochemistry. Results: Ninety-six genes were upregulated in diabeticlomeruli, whereas 519 genes were downregulated. The list of overexpressed genes in DN includes aquaporin 1,alpain 3, hyaluronoglucosidase, and platelet/endothelial cell adhesion molecule. The list of downregulated genesncludes bone morphogenetic protein 2, vascular endothelial growth factor (VEGF), fibroblast growth factor 1,nsulin-like growth factor binding protein 2, and nephrin. A decrease in VEGF and nephrin could be validated at therotein level and also at the RNA level in renal biopsy specimens from 5 additional patients with diabetes.onclusion: Results of oligonucleotide microarray analyses on control and diabetic glomeruli are presented andiscussed in their relation to vascular damage, mesangial matrix expansion, proliferation, and proteinuria. Ourndings suggest that progression of DN might result from diminished tissue repair capability. Am J Kidney Dis 43:36-650.2004 by the National Kidney Foundation, Inc.

NDEX WORDS: Diabetic nephropathy (DN); expression profile; messenger RNA (mRNA); kidney; glomeruli;
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pathology/kidney/diabeticnephropathy/).

IABETIC NEPHROPATHY (DN) is a ma-jor cause of morbidity in patients with

ype 2 diabetes.1 One of the earliest clinical signsf DN is microalbuminuria, which oftenrogresses toward proteinuria.2 Characteristic fea-ures associated with DN include hyperfiltration,ollowed by a decrease in glomerular filtrationate (GFR), glomerular hypertrophy, progressivexpansion of the mesangial matrix, and thicken-ng of the glomerular and tubular basement mem-

From the Department of Pathology, Leiden Universityedical Center, Leiden, The Netherlands; and Department

f Medicine and Therapeutics, Mater Misericordiae Hospi-al, University College Dublin and Dublin Molecular Medi-ine Centre, Dublin, Ireland.

Received August 12, 2003; accepted in revised formecember 18, 2003.Address reprint requests to Hans J. Baelde, BSc, Leiden

niversity Medical Center, Department of Pathology, POox 9600, Bldg 1, L1-Q, 2300 RC Leiden, The Netherlands.-mail: [email protected]© 2004 by the National Kidney Foundation, Inc.0272-6386/04/4304-0007$30.00/0

tdoi:10.1053/j.ajkd.2003.12.028

American Journal o36

ranes.3 These features may precede the develop-ent of glomerulosclerosis and interstitial fibrosis

nd, eventually, the onset of end-stage renalisease (ESRD).Little is known about the molecular mecha-

isms leading to ESRD in DN. Although the rolef many genes in progressive renal diseases haseen described,5,6 their interrelationship remainsargely unclear. With the completion of the hu-an genome project and the development oficroarray technology, it now is possible to

imultaneously screen the RNA expression ofhousands of genes in healthy and diseased or-ans or in parts of them. Although gene-profilingtudies have been described recently in animalodels for DN,7 microarray studies of isolated

lomeruli from human diabetic kidneys have noteen reported.In this study, we investigate the gene expres-

ion profile of glomerular RNA from patientsith type 2 diabetes mellitus and glomerularNA from individuals with normal renal func-

ion and histological characteristics.

METHODS

atientsCadaveric donor kidneys were obtained from Eurotrans-

lant. These kidneys were unsuitable for transplantation for
echnical or morphological reasons (Table 1). We used
f Kidney Diseases, Vol 43, No 4 (April), 2004: pp 636-650

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GENE PROFILING IN HUMAN DIABETIC GLOMERULI 637

lomeruli from 2 control kidneys and 2 kidneys from pa-ients with diabetes mellitus type 2. DN was confirmedistologically by periodic acid–Schiff–stained paraffin sec-ions. Pathological criteria for DN includes glomerularypertrophy, diffuse mesangial and focal nodular glomerulo-clerosis, arteriolar hyalinosis, focal and segmental glomeru-osclerosis, hyaline drops between Bowman’s capsule andpithelial cells, and interstitial fibrosis. Nodular glomerulo-clerosis and arteriolar hyalinosis are characteristic for DNnd present in the diabetic kidneys we used for this study.

solation of GlomeruliGlomeruli were isolated as described earlier.8 In brief,

resh cortical tissue was first pressed with a flattened glassestle through a 212-�m pore diameter metal sieve and thenhrough a 150-�m pore diameter metal sieve. Glomeruliere rinsed from the surface of the 150-�m sieve with

ce-cold phosphate-buffered saline (PBS), transferred to aube, and pelleted for 1 minute at 1,200g. Supernatant wasemoved, and glomeruli were frozen at �70°C until RNAsolation. The purity of the glomerular suspension wasontrolled by light microscopy and was at least 90%.

NA IsolationGlomerular RNA was isolated using a combination of 2

NA isolation procedures. Glomerular tissue (500 mg) wasissolved in 5 mL of Trizol (Invitrogen Corp, Carlsbad, CA)nd homogenized with an ultra-turrax (Janke & Kunkel,taufen, Germany) for 1 minute. After adding 1 mL ofhloroform and mixing for 1 minute, the suspension wasentrifuged at 15,000g for 10 minutes. RNA was precipi-

Table 1. Pati

Control 1

etinopathy Nouration of diabetes type 2 (y) —ge (y) 29ex Maleerum creatinine (mg/dL)* 0.68erum glucose(mg/dL)* 133rine glucose* �rine protein* �FR (mL/min)* 181erfusion fluid UWold ischemia time (h) 32opamine (�g/kg BW/min) 3nown other drugs —ause of death ICBeason of refusal Lesion upper arterialclerotic glomeruli (%) �1

nterstitial fibrotic area (%) �5

NOTE. To convert serum creatinine in mg/dL to �mol/L, mAbbreviations: UW, University of Wisconsin solution; ICB*Levels within last 24 hours of donation.

ated with isopropanol. The pellet was air dried, dissolved in w

00 �L of sterile distilled water, and purified further with anNeasy Mini column (Qiagen GmbH, Hilden, Germany)ccording to instructions of the manufacturer.

To assess RNA quality, 2 �g of RNA was applied on a 1%garose-formalin gel. Electrophoresis was performed for 3ours at 50 V. The gel was stained with ethidium bromide.

icroarray HybridizationHybridizations were performed on the Human Genome

95Av2 Array (Affymetrix, Santa Clara, CA). This arrayontains approximately 12,000 sequences characterized pre-iously in terms of function or disease association. Tenicrograms of total RNA from isolated glomeruli of each

idney was converted to complementary DNA and doubletranded DNA and transcribed in vitro according to theanufacturer’s instructions. After hybridization, microchipsere scanned and analyzed using Affymetrix Microarrayuite 5.0 software. To normalize data from different microar-ay experiments, expression levels of all genes on the chipere scaled to a standard value, and the mean of the scaling

actors was calculated. This value served as the normaliza-ion factor for all genes represented on the different microar-ay chips. To obtain normalized expression values, expres-ion levels for each gene were multiplied by the normalizationactor. Statistics behind this method can be found in the

icroarray Suite User’s Guide, version 5.0, available atttp://www.affymetrix.com/support/technical/manuals.affx.o determine the interassay variation, the labeling procedurend hybridization for 1 of the controls and 1 of the diabeticlomerular samples were performed in duplicate. A total of 6hips were hybridized; 3 chips with control RNA and 3 chips

aracteristics

Control 2 Diabetes 1 Diabetes 2

No Yes Yes— �5 �570 55 63

Male Male MaleUnknown 1.14 Unknown

128 326 Unknown� ��

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by 88.4; glucose in mg/dL to mmol/L, multiply by 0.05551.erebral bleeding; BW, body weight.

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onfirmation of Microarray Data by Real-Timeolymerase Chain ReactionWe performed real-time polymerase chain reaction (PCR)9

n combination with the Taqman probe (Applied Biosys-ems, Foster City, CA) technique for 3 genes to confirm databtained with microarray analyses. RNA (1 �g) was con-erted to complementary DNA by using avian myeloblasto-is virus reverse transcriptase (Roche Applied Science).ranscription levels for nephrin, transforming growth fac-

or-� (TGF-�), and vascular endothelial growth factorVEGF) were determined and corrected to a panel of 5ifferent housekeeping genes, ie, glyceraldehyde-phosphate-ehydrogenase, �2-microglobulin, hypoxanthine phosphori-osyl transferase, porphobilinogen deaminase, and TATA-inding protein, as described by Vandesomple et al.10 Primernd probe sequences are listed in Table 2. To calculate theelative messenger RNA (mRNA) levels, we measured thehreshold cycle values of a standard curve with a knownmount of total RNA. For each housekeeping gene, theelative amount of samples was calculated by linear regres-ion analysis from their standard curve. Relative values ofach of the 5 different household genes of controls weredjusted to 1 by dividing the samples by the mean of allamples. After this correction, the mean of the 5 differentousekeeping genes was calculated. Relative expressionevels of VEGF, TGF-�, and nephrin were calculated byividing the value of the gene by the mean of the differentousehold genes. Relative values were set to 1 for theontrols.

We also measured relative mRNA levels for VEGF, TGF-�,nd nephrin in microdissected glomeruli from 5 renal biopsyamples from patients with DN according the method ofpecht et al.11 In brief, 4-�m frozen sections were put on aolyethylene foil-coated slide. To microdissect glomeruli,e used the PALM Laser-MicroBeam System (P.A.L.M.,olfratshausen, Germany). RNA from the microdissected

lomeruli was isolated using the Trizol method, as de-cribed. All 5 patients with diabetes had type 2 diabetes fort least 5 years, with retinopathy and DN. Renal biopsypecimens from these patients showed glomerular hypertro-hy, diffuse mesangial and focal nodular glomerulosclerosis,rteriolar hyalinosis, focal and segmental glomerulosclero-is, and interstitial fibrosis. Relative mRNA levels for VEGF,GF-�, and nephrin in microdissected diabetic glomeruliere compared with those in glomeruli from 8 control

amples, described previously.12

mmunohistochemistryTo validate differences in mRNA for VEGF and nephrin

t the protein level, immunohistochemical staining waserformed using specific antibodies. For VEGF staining,-�m paraffin sections of control and diabetic kidneys wereut. After removing the paraffin, sections were pretreatedith 0.4% pepsin for 20 minutes at 37°C. For nephrin

taining, we used 3-�m cryostat sections. Slides were washedn PBS and incubated for 2 hours at room temperature withhe primary antibody, diluted in 1% bovine serum albumin inBS (rabbit anti-nephrin, 1:1000, a gift of Dr Kawachi12;abbit anti-VEGF, 1:100; Santa Cruz Biotechnology, Santa
ruz, CA). After washing with PBS, slides were incubated T V N G B H P T d

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or 30 minutes with horseradish peroxidase–conjugated anti-abbit Envision (Dako, Glostrup, Denmark). Slides wereashed in PBS, and the staining was developed with diami-obenzidine. Color was enhanced by rinsing the slides in.5% copper (II) sulfate (CuSO4) solution for 5 minutes.fter counterstaining with hematoxylin, the slides wereehydrated and mounted.

tatisticsTo determine the reproducibility of the individual microar-

ay analyses within and between groups (ie, the control andN groups), we calculated coefficients of correlation. Clus-

ering analysis was performed using Spotfire 7.1 softwareSpotfire Inc, Cambridge, MA). We used z score normaliza-ion to normalize our data. The normalized value for gene as calculated as:

a) � (a � mean value of all samples for gene A)/

standard deviation (A)

here (a) is the normalized value, a is the value of sample aor gene A. If all values for gene A are identical, then allalues for gene A are set to zero. These normalized expres-ion values of the 6 different arrays were analyzed in annsupervised fashion by using the hierarchical clusteringethod with complete linkage and correlation. Data were

rdered by average value and visualized in a dendrogram.To identify genes of which expression was altered consis-

ently in the diseased samples, we used either genes presentn all 6 chips, those present in all 3 control samples andbsent in all 3 diabetic samples, or those absent in all 3ontrol samples and present in all 3 diabetic samples. Wesed multiple pairwise comparisons between control andisease groups by using the OpenStat statistics packageIowa State University, Ames, IA). We selected only genesor which the mRNA level showed an at least a 2-foldifference between controls and diabetic samples (Student’s-test, P � 0.01).

Gene clustering on the basis of Gene Ontology (GO) todentify gene clusters on the basis of gene function was per-ormed using the MAPPfinder1.0 program 13. MAPPFinder,hich can be downloaded from http://www.genmapp.org, isprogram that works in combination with GenMAPP andO to identify global biological trends in gene expressionata. MAPPFinder relates microarray data to each term inhe GO hierarchy, calculating the percentage of geneshanged for each GO biological process, cellular compo-ent, and molecular function term. Using this percentagend a z score based on the mean and SD of the hypergeomet-ic distribution, the user can order by GO function with theighest z score. This z score is calculated as:

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f genes in this specific GO term, and r is the number ofenes meeting the criterion in this specific GO term.Statistical analysis for real-time data was performed using

-way analysis of variance (ANOVA), and P less than 0.01s considered significant.

RESULTS

atient Characteristics

Donor characteristics are listed in Table 1.oth kidneys with DN were obtained from pa-

ients with a clinical history of type 2 diabetes fort least 5 years. Sex, cold ischemia time, type oferfusion fluid used, and cause of death wereimilar for the patients. Serum glucose levels ofontrol patients were normal, whereas glucoseevels in patients with diabetes were elevated (upo 326 mg/dL [18.1 mmol/L]). Consistent with aiagnosis of DN, urine protein levels in patientsith diabetes were increased. Control kidneys

howed normal morphological characteristicsithout histological abnormalities. Both diabeticidneys showed glomerular hypertrophy, diffuseesangial and focal nodular glomerulosclerosis

n 20% to 30% of glomeruli, arteriolar hyalino-is, and focal and segmental glomerulosclerosis.nterstitial fibrosis was seen in 25% to 50% ofhe tubulointerstitial area (Fig 1).

ene Expression Profiles of Control andiabetic Glomeruli

From the approximately 12,000 genes dis-layed on the microchip, 2,042 genes gave aositive signal on all 3 chips after hybridizationith RNA from the kidneys with DN. In glo-eruli from control kidneys, 4,297 genes gave a

ositive signal in all 3 samples after hybridiza-ion. Correlation coefficients between duplicateontrol samples and duplicate diabetic samplesere 0.972 and 0.932, respectively. A graph of

he correlation between a duplicate of control 1 ishown in Fig 2. Correlations between controls 1nd 2 and the 2 different diabetic samples alsoere high (0.930 and 0.900, respectively). Theean of correlations between the different con-

rol samples and different diabetic samples wasower (0.731). Unsupervised hierarchical cluster-ng of expression data as visualized in a dendro-ram (Fig 3) shows the same relations betweenamples. This dendrogram is based on the simi-arity between different samples. By this method,
he software recognized the highest similarity

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BAELDE ET AL640

etween duplicate hybridizations, among all 3ontrols, and among all 3 diabetic samples.

Using statistics mentioned in the Methodsection, we end up with a list of 96 candidateenes that were increased in DN and 519 geneshat were decreased. The top 50 upregulatedenes in diabetic glomeruli are listed in Table 3ratios varying between 2.3- and 4.9-fold). The0 most downregulated genes are listed in Table(ratios varying between 6.6- and 22.8-fold). In

hese lists, the unidentified expressed sequenceags (ESTs) are not shown. A list of all signifi-antly upregulated and downregulated genes cane found at www-onderzoek.lumc.nl/pathology/idney/diabeticnephropathy/.

Fig 1. Light microscopic pictures of a glomerulus froB) a diabetic kidney. Diabetic kidneys show glomlomerulosclerosis, and arteriolar hyalinosis. (Periodic

Analysis of genes that were either increased orecreased using MAPPfinder was performed toluster the genes on basis of their GO function.esults are listed in Table 5. If we look at resultsf decreased genes, there is a high z score forctin cytoskeleton and actin binding GO functionnd for nucleobase, nucleoside, nucleotide, anducleic acid metabolism. The increased genesre especially related to homeostasis and phospha-ases.

onfirmation of Microarray Data by Real-TimeCR

To validate results obtained with microarray,e performed real-time PCR assays for several

a control kidney and a representative glomerulus fromhypertrophy, diffuse mesangial and focal nodular

Schiff staining; original magnification �200.)

Fig 2. A graph of the cor-relation between duplicatemeasurements of a controlkidney. Each point repre-

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ranscripts. Results of the quantification of mRNAevels for TGF-�, nephrin, and VEGF are shownn Fig 4. With microarray and real-time PCR,atios between TGF-�1 from controls and dia-etic kidneys were found not to be significant1.87 versus 1.82; P � 0.01). Array analysishowed that nephrin was downregulated (7.3-old) in DN. Real-time PCR for nephrin alsohowed a decrease (15.4-fold; P � 0.01 com-ared with controls) in DN. For VEGF, ratiosere 19.5 and 14.2 (P � 0.01 compared with

ontrols), respectively. There was no significantifference between ratios measured using theicroarray and real-time PCR techniques. We

lso confirmed our data in an independent andarger patient group. Results of these measure-ents are shown in Fig 5. We found significant

ecreases of 2.75 times for nephrin and 2.25

Fig 3. A dendrogram of unsupervised hierarchicaatterns of the 6 different arrays show the degree of relscore for each gene. The normalized z score for geneene A)/STD (A), where (a) is the normalized value apregulated genes, and red indicates downregulated ge; C2, control 2; D1a, diabetes 1; D1b, duplicate of diab

imes for VEGF (P � 0.05). k

mmunohistochemistry

Results for VEGF and nephrin at the RNAevel were investigated further at the proteinevel by using immunohistochemistry. In normalidneys, VEGF and nephrin showed intense epi-helial staining along the peripheral capillaryoops of glomeruli (Fig 6A and C). VEGF alsohowed weak staining in some tubular epithelialells. In glomeruli of diabetic kidneys, stainingor both VEGF and nephrin was weaker or ab-ent (Fig 6B and D).

DISCUSSION

In this study, we describe gene profiles ofontrol and diabetic glomeruli from human kid-eys. RNA was extracted from isolated glo-eruli of cadaveric donor kidneys. These kid-

eys were unsuitable for transplantation for non–

ering on the basis of similarity in gene expressionhips of samples. Different colors show the normalizedalculated as: (a) � (a � mean value of all samples fors the value of sample a for gene A. Green indicatesbbreviations: C1a, control 1; C1b, duplicate of controlD2, diabetes 2.

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BAELDE ET AL642

Table 3. Top 50 of the Most Increased Genes in All Diabetic Glomeruli Versus Controls

Accession No. Ratio Gene Name Gene Function

AI547258 4.9 Metallothionein 2A Protects against heavy-metal toxicityU96078 4.7 Hyaluronoglucosaminidase 1 Involved in cell proliferation, migration, and

differentiationM16941 4.5 Major histocompatibility

complex, class II, DRAntigen presentation

U02388 4.3 Cytochrome P-450, subfamilyF, polypeptide 2

Leukotriene B4 omega-hydroxylase

X85030 3.9 Calpain 3 Skeletal muscle–specific calcium-dependent cysteineprotease

D17793 3.8 Aldo-keto reductase family 1 Catalyze the conversion of aldehydes and ketonesU19599 3.6 B–cell

lymphoma-2–associated Xprotein

An apoptotic activator

X58288 3.3 Protein tyrosine phosphatase,receptor type, M

Signaling molecules that regulate a variety of cellularprocesses, including cell growth, differentiation,mitotic cycle, and oncogenic transformation

D83402 3.2 Prostacyclin synthase Vasodilator and inhibitor of platelet aggregationH94881 3.2 FXYD domain-containing ion

transport regulator 2Gamma subunit of the Na�K�-ATPase

AB020722 3.0 Rho guanine exchange factor15

Form a complex with G proteins and stimulateRho-dependent signals

M25915 3.0 Clusterin (complement lysisinhibitor)

Has a role in the terminal complement reaction

J05257 3.0 Dipeptidase 1 Zinc-dependent metalloproteaseX16832 2.9 Cathepsin H Lysosomal cysteine (thiol) proteinaseD13640 2.8 Protein phosphatase 1F Inactivates the p21-activated kinaseD87002 2.8 Immunoglobin light chain InflammationAB018258 2.8 Adenosine triphosphatease,

class V, type 10BATPase activity

U95299 2.7 Notch homolog 4 (Drosophila) Has multiple epidermal growth factor like, notch, andankyrin repeats

L48215 2.7 Hemoglobin, beta Transports oxygen and carbon dioxideL11702 2.7 Glycosylphosphatidylinositol

specific phospholipase D1Hydrolyzes inositol-PO4 linkage in Ptdins-glycan

anchored proteinsU09577 2.7 Hyaluronoglucosaminidase 2 Lysosomal enzymeY07846 2.7 Growth arrest–specific 2 like 1 An actin-associated protein expressed at high levels in

growth-arrested cellsX64559 2.6 Tetranectin Functions in mineralization during osteogenesisL13720 2.6 Growth arrest-specific 6 Involved in the stimulation of cell proliferationM73554 2.6 Cyclin D1 Alters cell-cycle progressionAI762547 2.5 Protein phosphatase 3 Ca2�-dependent modifier of phosphorylation statusM93311 2.5 Metallothionein 3 Inhibits cortical neuron survival and neurite formationJ03910 2.5 Metallothionein 1G Protect against reactive oxygen species and metalsX58022 2.5 Corticotropin-releasing

hormone–binding proteinInhibits stimulation of pituitary adrenocorticotropic

hormone releaseD90144 2.5 Macrophage inflammatory

protein 1-�Small inducible cytokine

X58288 2.5 Protein tyrosine phosphatase,receptor type, M

Signaling molecules that regulate a variety of cellularprocesses, including cell growth, differentiation,mitotic cycle, and oncogenic transformation

L33930 2.5 CD24 antigen Glycosyl phosphatidylinositol-linked glycoprotein thatdifferentiates and activates granulocytes and Blymphocytes

AB009698 2.5 Solute carrier family 22 Renal p-aminohippurate/alpha-ketoglutarate exchangerAA100961 2.5 PECAM1 Transendothelial migration of leukocytes, angiogenesis,

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hat these kidneys have been exposed to isch-mia, which can alter gene expression.14 For thiseason, we compared diabetic kidneys with con-rol kidneys that underwent the same handlingefore the isolation of glomeruli. Isolation waserformed on ice and took approximately 5 to 10inutes for each kidney. From other studies, it is

nown that handling glomeruli on ice within 3ours does not alter mRNA expression for sev-ral profibrotic genes.15 The labeling procedurend hybridization from 1 of the controls and 1 ofhe diabetic glomerular samples were performedn duplicate to calculate interassay variation. Theorrelation coefficient was near 1, indicating thathe labeling and hybridization procedure is highlyeproducible. Correlations between different con-rol samples and between different diabeticamples also were very high, indicating rela-ively low heterogeneity within groups. Con-ersely, the correlation coefficient between con-rol and diabetic samples was lower, reflectingreater heterogeneity between groups. This alsoas found with hierarchical clustering analyses

Fig 3). By unsupervised analysis of data, the

Table 3. Top 50 of the Most Increased Genes

Accession No. Ratio Gene Name

U67733 2.5 Phosphodiesterase 2A

U45973 2.5 Kidney-enriched inositolphosphatase

X07732 2.5 HepsinAI017574 2.4 Cysteine-rich protein 1U03056 2.4 Hyaluronoglucosaminidase 1

M13929 2.4 c-mycU21931 2.4 Fructose-1,6-biphosphataseL06139 2.4 Tyrosine kinase, endothelial

S53911 2.4 CD34 antigen

AB002438 2.4 Semaphorin 6BU41518 2.4 Aquaporin 1U11863 2.4 Amiloride binding protein 1AJ001015 2.4 Receptor (calcitonin) activity

modifying protein 2AF004230 2.3 Leukocyte immunoglobulin-

like receptor B1U40391 2.3 Serotonin N-acetyltransferasX65784 2.3 Cell matrix adhesion regulat

Abbreviations: ATPase, adenosine triphosphatase; PEC

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ontrol and diabetic samples based on their corre-ation. These findings support the idea that givenbservations that the interassay variation andariation of the gene expression of samples withingroup are relatively low, such factors as isch-

mia, technical procedure, and biological varia-ion probably influence expression data to only aimited extent.

To confirm the data obtained from the microar-ay, we performed real-time PCR for nephrin,EGF, and TGF-�1. Relative levels for nephrin

nd VEGF were significantly decreased in DNompared with controls. No significant differ-nces were observed between real-time measure-ents and microarray results. With both tech-

iques, the difference in TGF-� between controlsnd diabetic kidneys was found not to be signifi-ant. To validate that our findings obtained withadaveric donor kidneys apply to renal biopsyaterial, we also measured mRNA levels of

ephrin and VEGF in renal biopsy specimensrom 5 patients with DN and 8 controls. Theseatients similarly showed a decrease in messageor nephrin and VEGF. To show where the pro-

Diabetic Glomeruli Versus Controls (Cont’d)

Gene Function

Hydrolyzes cyclic adenosine monophosphate and cyclicguanine monophosphate

May negatively regulate actin cytoskeleton

Transmembrane serine proteaseMay function as a zinc carrier proteinInvolved in cell proliferation, migration and

differentiationPromotor for c-mycFructose-1,6-biphosphataseCritical for endothelial cell–smooth muscle cell

communication in venous morphogenesisCell-surface antigen expressed on hematopoietic stem

cells and vascular endotheliumMigrationWater channel proteinDeaminates putrescine and histamineInvolved in core glycosylation

Binds cellular and viral major histocompatibility complexclass I antigens

Enzyme in melatonin synthesisPromotes adhesion of cells to components of the

extracellular matrix

latelet/endothelial cell adhesion molecule 1.

in All

eor

ein was present, immunohistochemistry was per-

BAELDE ET AL644

Table 4. Top 50 of the Most Decreased Genes in Diabetic Versus Control Glomerular Samples

Accession No. Ratio Gene Name Gene Function

L76465 22.8 Hydroxyprostaglandindehydrogenase 15

Inactivates many prostaglandins

M11810 20.6 2�-5� Oligoadenylate synthetasegene

Catalyze the synthesis of 2�,5� oligomers ofadenosine

M63978 19.5 VEGF Mitogen for vascular endothelial cellsM22489 18.3 BMP-2 Member of the TGF-� family of growth factorsY16241 15.5 Nebulette Binds to actin, tropomyosin, and �-actininAF009314 15.1 TUAB Cri-du-chat region UnknownAF042377 15.0 Mannose 4,6-dehydratase Has a role in the synthesis of fucosylated

oligosaccharidesZ48541 14.7 Glomerular epithelium protein 1 Receptor-type protein tyrosine phosphataseAI207842 14.3 Prostaglandin D2 synthase Catalyzes synthesis of prostaglandin DJ03779 14.0 Membrane metallo-endopeptidase Inactivates several peptide hormones, including

glucagonL13698 13.6 Growth arrest-specific 1 Has a role in growth suppressionL12468 13.5 Aminopeptidase A Glycosylated zinc-dependent metalloproteaseX59065 12.7 FGF-1 Potent mitogen for a variety of cell typesU49392 12.5 Allograft inflammatory factor 1 Cytokine-inducible protein associated with

vascular injuryY07593 12.2 Coxsackie virus and adenovirus

receptorReceptor for coxsackievirus and adenovirus

AB014524 11.9 SLAC2-B UnknownD78014 11.9 Dihydropyrimidinase-like 3 Mediate signals involved in axonal outgrowthAF078096 11.3 Forkhead/winged helix-like

transcription factor 7Transcription factor

X81053 11.2 Collagen, type IV, � 4 Extracellular matrix protein that forms basementmembranes

X04371 10.9 2�,5�-Oligoadenylate synthetase 1 Catalyze the synthesis of 2�, 5� oligomers ofadenosine

AB029000 10.5 Sulfatase FP SulfataseU17034 10.4 Phospholipase A2 receptor 1 Secretory phospholipases A2 receptorL28997 9.7 Adenosine-di-phosphate-ribosylation

factor-like 1Stimulate phospholipase D

U65887 9.4 Cytosin-di-phosphate-diacylglycerolsynthase

Converts phosphatidic acid tocytosin-di-phosphate-diacylglycerol

S37730 9.3 Insulin-like growth factor bindingprotein-2

Binds to and modulates insulin-like growth factoractivity

X73608 9.2 Sparc/osteonectin(testican) Function may be related to protease inhibitionU24152 9.1 p21/Cdc42/Rac1-activated kinase 1 Regulates cell motility and morphologyX14034 9.0 Phospholipase C, gamma 2 Hydrolyzes phosphatidyl inositolM22489 8.6 BMP-2 Signals through receptor serine/threonine kinasesAF047419 8.4 Epicardin, podocyte-expressed 1 Transcription factorM97935 8.4 Signal transducer and activator of

transcription 1Transcription factor

L17418 8.3 Complement receptor type 1 Binds complementAB014605 8.2 Atrophin-1 interacting protein 1 Interact with atrophin-1X74819 8.2 Troponin T2 The tropomyosin-binding subunit of troponinM24594 8.1 Interferon-induced protein UnknownU42360 7.9 Putative prostate cancer tumor

suppressorPutative integral membrane tumor suppressor

proteinJ02931 7.8 Coagulation factor III Initiates the coagulation protease cascade

assembly and propagationM97936 7.7 Interferon-stimulated gene factor-3 Transcription factorL25124 7.6 Prostaglandin E receptor 4 Receptor that signals through a stimulatory G-

protein

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M


ormed for nephrin and VEGF. We found thatEGF and nephrin, in particular, were present inodocytes along the glomerular basement mem-rane. At the protein level, a decrease for theseolecules was detected, a finding in line with

hat at the RNA level.In diabetic kidneys, more genes were down-

Table 4. Top 50 of the Most Decreased Genes in

Accession No. Ratio Gene Name

AF022375 7.5 Vascular endothelial growth

U50534 7.5 Breast cancer 2 (BRCA 2)AB022918 7.2 �2,3-sialyltransferaseAB006746 7.1 Phospholipid scramblase 1

U18934 7.0 Protein tyrosine kinaseAJ001381 7.0 Myosin IBD17517 7.0 TYRO3 protein tyrosine kinaY08374 7.0 Cartilage glycoprotein-39

M62424 6.9 Coagulation factor II receptoJ02611 6.7 Apolipoprotein DAI401567 6.6 Glutamate receptor

Abbreviations: SLAC2-B, synaptotagmin-like protein hoTPase, adenosine triphosphatase; PECAM1, platelet/end

Table 5. Top 10 MAPPfinder Results Based o

GO Identification

APPfinder results based on decreased genes7242 I5515 P

15629 A4 B

6886 I5488 B8285 N3779 A3677 D6139 N

APPfinder results based on increased genes19725 H30005 D16302 P16791 P30006 H5505 H

16788 H4437 I7218 N

19730 A

egulated than upregulated compared with con-rols. This is in accordance with the fact that theumber of genes on the chip giving a positiveignal after hybridization (present genes) foriabetic glomeruli was lower than the number ofresent genes on the chips for controls (2,042ersus 4,297). These results suggest that down-

tic Versus Control Glomerular Samples (Cont’d)

Gene Function

Induces endothelial cell proliferation and vascularpermeability

Involved in DNA repair processesPlays a role in synthesis of sialyl-paraglobosidePlays a role in the EGF-induced metabolic or

mitogenic response.Receptor protein tyrosine kinaseMember of the myosin family of motor ATPasesReceptor protein tyrosine kinaseAssociated with monocyte to macrophage

maturationInvolved in platelet activationComponent of high density lipoproteinLigand-gated ion channel selectively permeable

to sodium and calcium

e lacking C2 domains-B; EGF, epidermal growth factor;l cell adhesion molecule 1.

Function Ranked on Basis of Highest z Score

GO Name z Score

lular signaling cascade 2.14binding 2.11toskeleton 2.07al process unknown 1.97lular protein transport 1.95

1.89e regulation of cell proliferation 1.83nding 1.83ding 1.81ase, nucleoside, nucleotide, and nucleicetabolism

1.78

tasis 4.81alent inorganic cation homeostasis 4.30atase 4.25oric monoester hydrolase 3.84etal ion homeostasis 3.72etal binding 3.19

se, acting on ester bonds 3.13phosphatidylinositol phosphatase 3.03eptide signaling pathway 3.03robial humoral response 3.03

Diabe

factor

se

r

mologu

n GO

ntracelroteinctin cyiologic

ntracelindingegativctin biNA binucleobacid m

omeosi-, tri-vhosphhospheavy meavy mydrola

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BAELDE ET AL646

egulation of genes occurs considerably moreften in the development of DN. This idea isupported by the results of MAPPfinder. Nucleo-ide metabolism is in the top 10 of decreased GOunctions. There also is a reduction in DN at theRNA level of genes involved in the formation

f the actin skeleton. Downregulation of theseathways in DN might account in part for theeneral downregulation seen for many other genesn the chip. Another reason for the difference inumber of present genes between diabetic andontrol samples might be the stringency withhich microarray analyses were performed. Weanted to be sure to include only highly reproduc-

Fig 5. Nephrin and VEGFRNA levels measured with

eal-time PCR in an indepen-ent group of 5 patients withN. Relative levels wereompared with a panel of 8ontrol kidneys. *P < 0.05ompared with control (Stu-
ent’s t-test).
ble data in our list of differentially expressedenes. Therefore, only genes present in all 3iabetic arrays were included. There is moreeterogeneity in gene expression patterns amongiabetic samples than control samples. This wouldean that because of this difference in heteroge-

eity, the chance that a certain gene is positive onll 3 chips in the diabetic group is less than theame gene being present in all 3 chips of normalamples.

One of the major clinical problems in patientsith diabetes is the presence of vascular abnor-alities, such as increased endothelial permeabil-

ty to macromolecules and endothelial prolifera-

Fig 4. Validation of mi-croarray results for TGF-�1,nephrin, and VEGF by real-time PCR. Data have beennormalized for a panel of5 different housekeepinggenes, whereas array dataare normalized for total chipsignals and compared withcontrol kidneys. *P < 0.001compared with control (1-way ANOVA).

tpenicmttmlafhclmb

eb(eansclk

pbpdEm

sgok


ion.16 Considerable research has focused on theathogenesis of endothelial dysfunction, but thexact mechanisms have remained unclear untilow. VEGF is one of the most important factorsn endothelial repair and angiogenesis. It re-ently was shown that subtotally nephrecto-ized rats show a reduction in VEGF mRNA in

he kidney.17 Treatment of these rats with angio-ensin-converting enzyme inhibitors leads to nor-alization of both glomerular VEGF mRNA

evels and capillary endothelial cell density. Innimal models for DN, an increase in VEGF wasound in diseased renal tissue.18 Conversely, inuman renal biopsy specimens with DN, a de-rease in VEGF at both the protein and mRNAevels was shown.19 The notion that VEGFRNA was decreased in human DN is supported

20

Fig 6. Representative photographs of renal tissue sections of glomeruli of a (A) control or (B) diabetic kilomerular podocytes. This staining was reduced in thn frozen sections of a (C) control or (D) diabetic kidnidney. (Original magnification �200.)

y our observations. Another gene for which m

xpression was significantly decreased in dia-etic glomeruli is fibroblast growth factor 1FGF-1). This protein functions as a modifier ofndothelial cell migration and proliferation andn angiogenic factor, and it can protect the kid-ey against ischemia-reperfusion injury.21 Expres-ion of platelet/endothelial cell adhesion mole-ule 1, which is involved in angiogenesis andeukocyte trafficking, was increased in diabeticidneys.Accumulation of extracellular matrix (ECM)

roteins has been found in animal models andiopsy specimens from patients with DN.22 Ex-ansion of the ECM can be the result of aisturbed balance between ECM synthesis andCM degradation or a combination of theseechanisms. Of note, we found an increase in

for VEGF or nephrin. (Top) VEGF staining on paraffinThe control kidney shows abundant VEGF staining inerulus of a diabetic kidney. (Bottom) Nephrin stainingphrin also was reduced in podocytes of the diabetic

taineddney.e glomey. Ne

essage for metargidin, a disintegrin metallopro-

t�gspwgmcmomirTmelmcTldtsbrg(cgch

mpWgmhIhBswiGlcc

ectostrslcsradhlrmdtTpo

gpflfrltdtVctlptbt

putaped

BAELDE ET AL648

einase,23 and a decrease in message for collagen4(IV), a major structural component of thelomerular basement membrane. In a previoustudy, an increase for overall collagen type IVrotein was observed in glomeruli from patientsith DN.3 In animal cell cultures under highlucose levels, an increase in collagen type IVRNA was mainly found for the �1, �3, and �5

hains.24 In this study, we did not find a change inRNA level for TGF-�. In the literature, the role

f TGF-� has been described in several animalodels (summarized in25), and a small increase

n mRNA level in human glomeruli has beeneported.26,27 A reason for the opposing result forGF-� between previous studies and our studyight be that this molecule was studied at differ-

nt stages of the disease. Alternatively, the mRNAevels for TGF-� we described in our studyight not reflect the level or activity of the

orresponding protein. An increase in activeGF-� also can be explained by increased trans-

ation or increased activation of latent TGF-�. Aecrease in natural inhibitors also can increasehe bioactivity of TGF-�. Recently, it also washown that high glucose levels can induce fi-ronectin and collagen type III expression inenal fibroblasts independent of TGF-�1.28 Therowth factor bone morphogenetic protein 2BMP-2), the growth factor inhibitor synde-an-2, and the growth factor receptor insulin-likerowth factor binding protein-2 were all de-reased in DN. These components are known toave a role in ECM remodeling.29-31

The diabetic kidneys analyzed in this studyorphologically showed glomerular hypertro-

hy and proliferation, a common event in DN.32

ith respect to proliferating cells in diabeticlomeruli, expression profiling of these glo-eruli as reported here shows many genes that

ave an important role in cell-cycle regulation.n kidneys with DN, we saw an increase inyaluronoglucosaminidase 1 and a decrease inMP-2 and growth arrest–specific 1 protein, all

uggestive of increased proliferation. It recentlyas shown that treatment of streptozotocin-

nduced diabetic rats with BMP-7 preserves theFR, reduces proteinuria, and prevents glomeru-

osclerosis.33 For breast cancer 2 (BRAC-2), ned-in, and the cytokines FGF-1 and VEGF, a role inell-cycle control has been described.34-36

The pathogenesis of albuminuria, one of the d

arliest clinical signs of DN, has not been fullylarified. It generally is assumed that the filtra-ion apparatus of the glomerular capillary wall isf central importance in this process. It has beenhown that the slit diaphragm located betweenhe foot processes of the podocytes has a crucialole in the filtration of macromolecules.37 Expres-ion of nephrin, a transmembrane protein thatocalizes in the slit pore of glomerular epithelialells, was decreased in diabetic glomeruli in ourtudy. This observation is in agreement with theeduction in glomerular nephrin gene expressionnd increase in albuminuria at a later stage of theisease in both human DN38,39 and diabetic andypertensive rats.40 Transcription of podoca-yxin, a protein expressed in the slit pore, isegulated by the transcription factor Wilm’s tu-or 1 (not present in the top 50, but 6.9-fold

ecreased).41 Downregulation of this transcrip-ion factor may lead to a lack of podocalyxin.hese findings support the hypothesis that slitore–associated proteins have a role in the devel-pment of proteinuria.In conclusion, we found that in DN, more

enes were downregulated than upregulated com-ared with controls, which might be explainedor a large part by decreased nucleotide metabo-ism. We also found a disturbed cytoskeletonormation in DN. Many other tissue repair–elated genes, such as BMP-2, FGF-1, insulin-ike growth factor binding protein-2, and connec-ive tissue growth factor (CTGF), wereownregulated in DN, all suggestive of reducedissue repair capacity. On top of that, message forEGF was decreased in DN compared with

ontrols. This finding for VEGF was validated athe protein level, and additionally, tissue VEGFevels were decreased in an independent group ofatients with diabetes. These findings suggesthat the progression of DN might, at least in part,e a result of a diminished repair mechanism inhe endothelium of the capillaries.

Results described in this study underscore theotential of gene chip technology as a method fornraveling the complexities of the renal responseo diabetes mellitus. This powerful techniquellows simultaneous analysis of the expressionrofile of thousands of genes. We discussed sev-ral genes differentially expressed between arrayata sets that are functionally related to vascular
amage, mesangial matrix expansion, prolifera-

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ion, and proteinuria, events seen in DN. Addi-ional elucidation of the functional involvementf these genes by studies of larger groups ofatients and time course experiments will lead ton even better understanding of the processeseading to diabetic nephrosclerosis.

ACKNOWLEDGMENT

The authors thank Dr Kawachi (Niigata, Japan) for provid-ng the anti-nephrin antibody and Wendy Bouwman andlaas Koop for immunohistological staining of VEGF andephrin.

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tg

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gene expression profiling in glomeruli from human kidneys with diabetic nephropathy

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