Abstract

Development of hypertension stems from both environmental and genetic factors wherein the kidney plays a central role. Spontaneously hypertensive rats (SHR) and the nonhypertensive Wistar-Kyoto (WKY) controls are widely used as a model for studying hypertension. The present study examined the renal gene expression profiles between SHR and WKY at a prehypertensive stage (3 wk of age) and hypertensive stage (9 wk of age). Additionally, age-related changes in gene expression patterns were examined from 3 to 9 wk in both WKY and SHR. Five to six individual kidney samples of the same experimental group were pooled together, and quadruplicate hybridizations were performed using the National Institute of Environmental Health Sciences Rat version 2.0 Chip, which contains ∼6,700 genes. Twenty two genes were found to be differentially expressed between SHR and WKY at 3 wk of age, and 104 genes were differentially expressed at 9 wk of age. Soluble epoxide hydrolase (Ephx2) was found to be significantly upregulated in SHR at both time points and was the predominant outlier. Conversely, elastase 1 (Ela1) was found to be the predominant gene downregulated in SHR at both time points. Analysis of profiles at 3 vs. 9 wk of age identified 508 differentially expressed genes in WKY rats. In contrast, only 211 genes were found to be differentially expressed during this time period in SHR. The altered gene expression patterns observed in the age-related analysis suggested significant differences in the vascular extracellular matrix system between SHR and WKY kidney. Together, our data highlight the complexity of hypertension and the numerous genes involved in and affected by this condition.

  • soluble epoxide hydrolase
  • hypertension
  • arachidonic acid
  • elastase
  • real-time polymerase chain reaction

hypertension is a major risk factor for cardiovascular disease, renal failure, and stroke and is associated with significant morbidity and mortality (6). Many systems and factors contribute to the regulation of blood pressure, such as the renin-angiotensin-aldosterone system, extracellular matrix, and endothelin. Alteration in the complex array of polygenic and environmental factors that regulate blood pressure results in hypertension. Such perturbations commonly affect salt homeostasis, intravascular volume, and systemic vascular resistance (23). Even with such a diversity of physiological systems that control blood pressure, the majority of genetic and acquired forms of hypertension involve the kidney (23, 28, 38). Indeed, renal transplantation studies demonstrating the transfer of the hypertension phenotype from donor to recipient highlight a key role of the kidney in this disease (15, 39). Investigation into the renal gene expression profiles that accompany hypertension will help identify potentially important causes of this disease and/or novel therapeutic targets.

Increased prevalence of hypertension with age coincides with changes in blood pressure patterns and reflects differences in hemodynamics between young and old hypertensives (1012, 34, 4143). For example, a shift from peripheral arterial resistance to arterial stiffness occurs with age (1012, 34, 4143). Besides environmental factors such as diet and lack of exercise, genetic studies provide evidence that intrinsic factors may also contribute to the development of hypertension with age. A common animal model used to investigate hypertension is the spontaneously hypertensive rat (SHR) and the normotensive control Wistar-Kyoto (WKY) rat strain (31). These animals exhibit similar age-dependent and end-organ damage phenotypes as observed in humans (28, 38).

We performed cDNA microarray analysis to investigate differences in renal gene expression between SHR and WKY rats at both a prehypertensive stage (3 wk of age) and during the developmental phase of hypertension in the SHR (9 wk of age). Although blood pressure is similar in SHR and normotensive controls at 3 wk of age, changes in glomerular function, pressure-natriuresis, and vascular structure are well documented in prehypertensive SHRs (8, 40, 46). At 9 wk of age, blood pressure is still rising rapidly in the SHR and is elevated relative to the WKY (46). Comparisons between WKY and SHR at each time point allow the detection of differentially expressed genes that might contribute to the distinct blood pressure and vascular and renal phenotypes at each age. A comparison of changes in the SHR between 3 and 9 wk of age allows for the detection of temporal gene changes that might be associated with the blood pressure changes during this period. Numerous differences in the profiles between SHR and WKY were observed and independently validated, as were temporal changes within each strain, thus identifying several potentially important genes involved in blood pressure regulation and the development of hypertension.

MATERIALS AND METHODS

Animals and RNA isolation.

Kidney RNA was isolated from individual male and female SHR and WKY rats at ages corresponding to the prehypertensive (3 wk) and hypertensive (9 wk) stages of life. SHR and WKY rats (3 and 9 wk old) were purchased from Charles River Laboratories (Wilmington, MA). Rats were anesthetized with methoxyflurane, abdominal cavities were opened, and kidneys were perfused with ice-cold PBS solution. Kidneys were rapidly removed, cut into small pieces, and immersed immediately in liquid nitrogen. All tissues were stored at −80°C until preparation of RNA. Total RNA was isolated using an RNeasy Midi kit (Qiagen, Valencia, CA) and concentrated using a Microcon YM-30 column (Millipore, Billerica, MA). A formaldehyde agarose gel containing ethidium bromide was used to assess the quality of the RNA.

Microarray hybridization.

The National Institute of Environmental Health Sciences (NIEHS) cDNA Rat version 2.0 Chip, which contains ∼6,700 genes, was used for gene expression profiling experiments. A complete listing of the genes on this chip is available at the following website: http://dir.niehs.nih.gov/microarray/chips.htm. The cDNA microarray chips were prepared as previously described (7). The spotted cDNAs were derived from a collection of sequence-verified IMAGE clones that spanned the 5′-end of the genes and ranged in size from 500 to 2,000 bp (Incyte Genomics, Palo Alto, CA). M13 primers were used to amplify insert cDNAs from purified plasmid DNA in a 100-μl PCR reaction mixture. A sample of the PCR products (10 μl) was separated on 2% agarose gels to ensure quality of the amplifications. The remaining PCR products were purified by ethanol precipitation, resuspended in ArrayIt Spotting Solution Plus buffer (Telechem, San Jose, CA), and spotted on poly-l-lysine-coated glass slides using a modified, robotic DNA arrayer (Beecher Instruments, Bethesda, MD). Each total RNA sample (15–75 μg), representing five to six individual animals per experimental group, was labeled with cyanine 3 (Cy3) or cyanine 5 (Cy5)-conjugated dUTP (Amersham, Piscataway, NJ) by a reverse transcription reaction using SuperScript RT (Invitrogen, Carlsbad, CA) and oligo(dT) primer (Amersham). The fluorescently labeled cDNAs were mixed and hybridized simultaneously to the cDNA microarray chip. Each RNA pair was hybridized to a total of four arrays employing a fluor reversal accomplished by labeling the control sample with Cy3 in two hybridizations and with Cy5 in the other two hybridizations. The cDNA chips were scanned with either an Axon Scanner (Axon Instruments, Foster City, CA) or an Agilent Scanner (Agilent Technologies, Wilmington, DE) using independent laser excitation of the two fluors at 532- and 635-nm wavelengths for the Cy3 and Cy5 labels, respectively. Pairwise comparisons were carried out as described in Table 1.

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Table 1.

Pairwise comparisons for microarray analysis

The raw pixel intensity images were analyzed using the ArraySuite version 2.0 extensions of the IPLab image processing software package (Scanalytics, Fairfax, VA). This program uses methods that were developed and previously described by Chen and coworkers (5) to locate targets on the array, measure local background for each target and subtract it from the target intensity value, and identify differentially expressed genes using a probability-based method. The data were filtered to provide a cut-off at the intensity level just above the buffer blank measurement values to remove those genes having one or more intensity values in the background range from further analyses. The ratio intensity data from all of the spots printed on the Rat Chip were used to fit a probability distribution to the ratio intensity values and estimate the normalization constants (m and c) that this distribution provides. The constant m, which provides a measure of the intensity gain between the two channels, indicated that the channels were approximately balanced near a value of 1.0. For each array, the ratio intensity values were normalized to account for the imbalance between the two fluorescent dyes by dividing the ratio intensity value by m. The other constant, c, estimates the coefficient of variation for the intensity values of the two samples. All arrays in this analysis had a c value of 0.12 or less. The probability distribution that is fit to the data was used to calculate a 95% confidence interval for the ratio intensity values. Genes having normalized ratio intensity values outside of this interval were considered significantly differentially expressed. For each of the four replicate arrays for each sample, lists of differentially expressed genes at the 95% confidence level were created and deposited in the NIEHS MAPS database (3). For each time point, a query of the database yielded a list of genes that were differentially expressed in at least three of the four replicate experiments. Any of these genes that indicated fluor bias or high variation were not considered for further analysis. Assuming that the replicate hybridizations are independent, a calculation using the binomial probability distribution indicated that the probability of a single gene appearing on this list when there was no real differential expression is ∼0.00048.

Hierarchical clustering was performed using Eisen's Cluster/Treeview software package (http://rana.lbl.gov; see Ref. 9). The entire data are available at the following website: http://dir.niehs.nih.gov/microarray/seubert/.

Independent validation by Northern analysis and RT-PCR.

The identity of microarray chip outlier cDNAs was confirmed by direct sequencing. Plasmid DNA was prepared using a QIAprep Mini-prep kit (QIAGEN) and completely sequenced using an ABI Prism BigDye DNA sequencing kit (Applied Biosystems, Foster City, CA). Sequence identity was confirmed using a BLAST search (National Center for Biotechnology Information/National Institutes of Health). Northern blot analysis was used to verify altered expression of RNAs in SHR and WKY kidney as previously described (25). Briefly, blots were probed with IMAGE clones (Research Genetics/Invitrogen) identified as outliers by microarray analysis. Fragments were isolated using a QIAquick Gel Extraction Kit (Qiagen), labeled with [α-32P]dCTP using a Random Primed DNA Labeling Kit (Roche Applied Science, Indianapolis, IN), and purified by NICK Columns (Amersham Biosciences). Autoradiographs were scanned, and relative RNA levels from SHR and WKY kidney were determined by normalization to β-actin expression. Statistically significant differences in the relative RNA levels between SHR and WKY kidney were determined using a Student's t-test. Values were considered significantly different at P < 0.05.

To address discrepancies between Northern blot and microarray chip results, we performed independent quantitative RT-PCR analysis of selected genes. Total RNA from individual WKY and SHR kidneys were treated with DNase I, and then 1 μg was used to prepare cDNA with the High-Capacity cDNA Archive Kit (Applied Biosystems). cDNA levels were detected using real-time PCR with the ABI PRISM 7900HT Sequence Detection System (Applied Biosystems) and SYBR Green I dye. Primers were created using the Oligo 6.4 program. (Supplemental data for this article may be found at http://ajprenal.physiology.org/cgi/content/full/00354.2004/DC1; see supplemental Table 10). For cDNA amplification, 1 ng cDNA was combined with 12.5 μl 2× SYBR Green Master Mix (Applied Biosystems), 1 μl forward and reverse primers (10 μM each), and 6.5 μl RNase-free water for a total volume of 25 μl. Samples were analyzed in triplicate, and a −RT sample was included with each plate to detect contamination by genomic DNA. Amplification was as follows: 1) 50°C, 2 min (for uracil-N-glycosylase incubation); 2) 95°C, 10 min (denaturation); and 3) 40 cycles of 95°C, 15 s, 60°C, 30 s (denaturation/amplification). Dissociation curves were also created by adding the following steps to the end of the amplification reaction: 95°C, 15 s (denaturation), 60°C, 15 s, and then gradually increasing to 95°C over 20 min, and finally holding at 95°C for 15 s. The degree of induction or repression was determined by quantitation of cDNA from target (SHR) samples relative to a calibrator sample (WKY). For all samples, the gene for glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the endogenous control for normalization of initial RNA levels. To determine this normalized value, 2−ΔΔCt values were compared between target and calibrator samples, where ΔCt = target gene (crossing threshold) Ct − GAPDH Ct, and ΔΔCt = ΔCtSHR − ΔCtWKY (24).

RESULTS

Cluster analysis.

Expression patterns were initially examined using cluster analyses generated from outlier lists of average gene expression ratio intensities at the 95% confidence level (Fig. 1). The expression patterns were remarkably similar between the quadruplicate hybridizations (data not shown). Interesting patterns emerged that highlight differences in expression profiles between the hybridization groups (Fig. 1). Region A represents genes that were downregulated from 3 to 9 wk of age in both SHR and WKY but were expressed at higher levels in SHR than WKY at both time points. In contrast, region C represents genes that were generally expressed at lower levels in SHR than WKY at 9 wk of age but were increased in expression in both SHR and WKY from 3 to 9 wk of age. Region B represents genes that do not change expression significantly in either SHR or WKY from 3 to 9 wk of age but are expressed at higher levels in SHR than WKY at 9 wk. Region D represents genes that were expressed in lower levels in SHR than WKY at 9 wk of age but did not show significant changes in expression from 3 to 9 wk in either SHR or WKY. Gene lists for each of these regions are found in the on-line data supplement. (Supplemental data for this article may be found at http://ajprenal.physiology.org/cgi/content/full/00354.2004/DC1).

Fig. 1.

Cluster analysis of microarray data. Data obtained from outlier lists of average gene expression ratio intensities at the 95% confidence level were used for cluster analysis. Genes with increased expression are shown in red, and genes with decreased expression are shown in green, with relative intensity corresponding to the degree of change. Columns represent data obtained from the different hybridization groups. Different highlighted regions (A-D) indicate examples of differences in expression patterns between the hybridization groups. Gene lists of these regions are found in the on-line data supplement (Supplemental data for this article may be found at http://ajprenal.physiology.org/cgi/content/full/00354.2004/DC1). The complete gene lists are found in the on-line data supplement. SHR, spontaneously hypertensive; WKY, Wistar-Kyoto.

Differentially expressed genes in 3-wk-old WKY vs. SHR and 9-wk-old WKY vs. SHR.

Microarray analysis comparing RNA isolated from 3-wk-old WKY vs. SHR animals identified 22 genes that were differentially expressed at the 95% confidence interval (Table 2). When analysis was performed comparing RNA from 9-wk-old WKY and SHR animals, 104 outliers were identified. A partial list containing outliers with the largest increase or decrease is found in Table 3. The complete gene lists of outliers are found in the on-line data supplement. (Supplemental data for this article may be found at http://ajprenal.physiology.org/cgi/content/full/00354.2004/DC1). Seven genes were differentially expressed between SHR and WKY at both 3 and 9 wk of age (Fig. 2). Soluble epoxide hydrolase (Ephx2), the gene with the largest increase in expression, was found to be significantly upregulated in SHR at both time points. Conversely, elastase 1 (Ela1), the gene with the largest decrease in expression, was found to be downregulated in SHR at both time points compared with WKY. Ninety-seven genes were differentially expressed between SHR and WKY only at 9 wk of age (Fig. 2). Examples include peroxisomal calcium-dependent solute carrier-like protein (Pcsc1), collagen type 1 (Col1a1), and cathespin L (Ctsl). Genes involved in lipid metabolism, such as CD36 antigen (downregulated) and apolipoprotein H (upregulated), were also differentially expressed in SHR vs. WKY at 9 wk of age. Glutathione-S-transferase mu type 2 (Yb2) exhibited lower expression in SHR at 9 wk consistent with its role in diastolic blood pressure regulation (48). The G protein binding protein critical renal failure gene exhibited higher expression levels in SHR at 9 wk and has been previously shown to be differentially expressed in renal disease (21).

Fig. 2.

Venn diagram showing strain-related gene expression changes in SHR vs. WKY. Data for the diagram were generated from outlier lists of average gene expression ratio intensities at a 95% confidence level. Values represent the no. of genes differentially expressed between SHR and WKY at 3 wk of age (left), 9 wk of age (right), and both 3 and 9 wk of age (middle). The complete lists can be found in the on-line data supplement (Supplemental data for this article may be found at http://ajprenal.physiology.org/cgi/content/full/00354.2004/DC1).

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Table 2.

Differentially expressed genes at 3 wk of age between SHR and WKY

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Table 3.

Differentially expressed genes at 9 wk of age between SHR vs. WKY

Age-related alterations in gene expression in WKY and SHR.

Separate microarray analyses were performed to compare gene expression changes between 3- and 9-wk-old WKY or between 3- and 9-wk-old SHR. A total of 508 genes were differentially expressed at the 95% confidence interval in WKY animals, whereas only 211 genes were differentially expressed in SHR. A partial list containing outliers with the largest increase or decrease in expression is found in Table 4 (for SHR and WKY). The complete gene lists of outliers are found in the on-line data supplement. (Supplemental data for this article may be found at http://ajprenal.physiology.org/cgi/content/full/00354.2004/DC1.) The most notable difference in the gene expression patterns between 3 and 9 wk of age in SHR and WKY was the greater number of changes observed in WKY animals. When comparing all the genes that were differentially expressed during this time period, only 174 were found in both outlier lists for SHR and WKY (Fig. 3). The majority of outliers observed in SHR were also present in WKY. Lysozyme (Lyz) was the predominant gene upregulated in both SHR and WKY between 3 and 9 wk of age but was upregulated to a much greater extent in WKY than in SHR. Decreased expression in collagen-related genes was prevalent in both SHR and WKY. For example, collagen type 1 (Col1a1) was the predominant gene downregulated in both SHR and WKY between 3 and 9 wk of age. However, increased expression of cathespin H (Ctsh), cathespin L (Ctsl), and elastase (Ela1) was observed only in WKY from 3 to 9 wk of age. In addition, cathespin L and elastase were found to have significantly lower expression in SHR compared with WKY (Tables 2 and 3).

Fig. 3.

Venn diagram showing age-related gene expression changes in SHR and WKY. Data for the diagram were generated from outlier lists of average gene expression ratio intensities at a 95% confidence level. Values represent the no. of genes differentially expressed only in SHR from 3 to 9 wk of age (left), only in WKY from 3 to 9 wk of age (right), and common in both WKY and SHR from 3 to 9 wk of age (middle). The complete lists can found in the on-line data supplement. (Supplemental data for this article may be found at http://ajprenal.physiology.org/cgi/content/full/00354.2004/DC1).

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Table 4.

Age-related changes in gene expression from 3 to 9 wk of age between SHR vs. WKY

Analysis of the expression patterns of genes that are involved in arachidonic acid metabolism or blood pressure regulation indicated few significant alterations (see supplemental Tables 2 and 3).

Independent validation of microarray results.

To validate the microarray results, we first confirmed the identity of 28 selected cDNAs used on the chip by direct sequencing (Table 5). cDNAs (100%) were found to be identical to the clone ID from the IMAGE collection. Next, we performed Northern blot analysis on RNAs prepared from individual SHR and WKY kidneys for 28 selected outliers. Actin was used to normalize relative changes on Northern blots, since no significant differences were observed in actin expression (AA859846) between SHR and WKY samples on the microarray chip. We observed excellent concordance between array data and Northern blot data with respect to the direction and magnitude of expression changes (Fig. 4 and Table 5). Our concordance rates were 73% for 3-wk samples and 77% for 9-wk samples. However, there were discrepancies between Northern blot and microarray data. Given that quantitative RT-PCR is a more sensitive method for validating array data than Northern blot, we performed RT-PCR analysis on 15 outliers. When there was a discrepancy between Northern blot and RT-PCR, we gave priority to the RT-PCR results in deciding whether or not an outlier gene was validated. We considered a gene to validate if a statistically significant difference was found, and it changed in the same direction as the microarray results. With this definition, our concordance rate was 89% (25 out of 28 genes). Of the genes we examined, the three that did not validate were expressed sequence tags.

Fig. 4.

Validation of microarray data by Northern analysis. A: Northern blot analysis of transcript expression for representative outliers with higher expression in SHR than in WKY at 9 wk. B: Northern blot analysis of transcript expression for representative outliers with lower expression in SHR than in WKY at 9 wk. C: Northern blot analysis of transcript expression for representative outliers with differential expression in SHR than in WKY at 3 wk.

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Table 5.

Gene expression validation

DISCUSSION

In the present study, we investigated renal gene expression profiles in SHR and WKY animals at prehypertensive (3 wk of age) and hypertensive (9 wk of age) stages. We identified 22 genes at 3 wk of age and 104 genes at 9 wk of age that were differentially expressed in SHR compared with WKY. Our study utilized the NCrINR SHR strain and yielded a similar list of differentially expressed genes at 9 wk of age as those reported by Okuda et al. (32) in the NCrj SHR strain at 10 wk of age. Our study was unique in that it identified age-dependent differences in renal gene expression between SHR and WKY animals. Most notable was the fewer number of genes that were differentially expressed between 3 and 9 wk of age in SHR (211) compared with WKY (508) kidneys, suggesting developmental differences in renal gene expression may be responsible, at least in part, for the SHR phenotype.

We found soluble epoxide hydrolase (sEH; EphX2) to be the predominant gene upregulated in SHR at both 3 and 9 wk of age. Consistent with other reports, these differences in sEH expression suggest an important role for eicosanoids in renal vascular homeostasis (17, 33, 45, 53, 54). Abundant expression of sEH is found in the kidney and localized to smooth muscle layers of the arterial wall (37, 50, 53). Arachidonic acid is first converted to epoxyeicosatrienoic acids (EETs) by cytochrome P-450 epoxygenases and then to corresponding diols [dihydroxyeicosatrienoic acids (DHETs)] by sEH. EETs have potent vasodilatory effects in the circulation and are thought to play a role in regulation of renal blood flow, arterial resistance, and systemic blood pressure by activating calcium-sensitive K+ channels (4, 16). Indeed, the P-450 epoxygenase metabolites are leading candidates for endothelial-derived hyperpolarizing factor (EDHF), the nitric oxide synthase- and cyclooxygenase-independent vasodilator that hyperpolarizes vascular smooth muscle cells (4, 16). In general, EETs are believed to be more biologically active than DHETs. Therefore, increased sEH expression, as occurs in SHR, would lead to enhanced EET hydrolysis and removal of vasodilatory eicosanoids. Recent studies have shown that the elevated blood pressure observed in SHR and in mouse models of hypertension was decreased after treatment with selective sEH inhibitors (17, 45, 54) and that sEH null mice have reduced blood pressure (45). Rat models of impaired renal hemodynamics have been associated with decreased EETs levels (19, 30). Together, these studies indicate that hydrolysis of the EETs to DHETs by sEH is an important mechanism for regulation of blood pressure. However, it remains unclear whether this is the key factor that initiates the hypertensive response in these animals.

A large diversity of physiological systems influences blood pressure, and alterations of any of these can result in hypertension (23). Interestingly, data generated from our microarray analysis did not reveal significant changes in most of the genes thought to be involved in blood pressure regulation, such as baroreceptors, the renin-angiotensin-aldosterone system, or natriuretic peptides. However, a moderate increase in expression of angiotensin receptor type II was observed in SHR at both 3 and 9 wk of age. We observed a few significant differences in genes commonly associated with a hypertensive response, such as decreased expression of GST mu 1 (Gstm1; see Refs. 27 and 48) and retinol binding protein 4 (Rbp4; see Ref. 29) and increased expression of β2-glycoprotein I (apolipoprotein H; see Refs. 13 and 44). Consistent with other reports, altered expression of CD36 was identified in SHR animals, suggesting a significant change in fatty acid metabolism (1, 35, 36). These data suggest other mechanism(s) may be involved in the spontaneous hypertension in this animal model. It should be noted that our analysis was limited to genes that were represented on our microarray chip; hence, we cannot rule out the possibility that changes in the expression of other genes might contribute to the hypertensive phenotype in SHR.

Analysis of age-related changes within each strain revealed a marked reduction in the number of genes differentially expressed in SHR animals compared with WKY. Aging is associated with many vascular changes, such as a rise in arterial stiffness, smooth muscle cell hypertrophy, and fibrosis (22). The regulation of expression of extracellular matrix proteins is important in the elastic properties of the vascular system, and an age-related increase in aortic stiffness has been observed in SHR (2, 14, 18, 26, 47, 49). Interestingly, we observed significant changes in the expression of several connective tissue genes from 3 to 9 wk of age in both WKY and SHR; however, the levels of procollagen (type I and II) and collagen (type I) were higher in SHR at both 3 and 9 wk of age. The age-related changes in metalloproteinases (e.g., Mmp14, gelatinase, TIMP2) observed in WKY were absent in SHR animals. In addition, the lower expression of elastase and cathespin L observed in SHR suggests a difference in the regulation of the vascular extracellular matrix system (20, 42). The reduction in expression of elastase in the current study is consistent with a reported reduction in blood pressure observed in SHR after injection of elastase (18). The altered expression of matrix genes appears to begin early in the SHR animals, since the differences between strains were apparent at 3 wk of age. It remains unknown whether the different gene expression patterns related to collagen and elastin contribute to development of hypertension or, alternatively, are the result mechanical factors stemming from the hypertension (26).

Differences in the gene expression profiles found in this and other studies reflect both genetic strain differences and environmental factors, all of which result in the same phenotype of spontaneous hypertension. Many of the significant differences in gene expression between SHR and WKY rats in the present study were found to occur at the prehypertensive stage (3 wk of age). Importantly, these data suggest that alterations in gene expression patterns occur in SHR before the onset of the hypertensive phenotype. The present data set provides evidence of potentially novel targets in blood pressure regulation and the development of hypertension, notably the important roles of sEH and extracellular matrix proteins.

GRANTS

This study was supported by the National Institute of Environmental Health Sciences Division of Intramural Research (D. C. Zeldin) and National Heart, Lung, and Blood Institute Grant HL-53994 (to D. L. Kroetz).

Acknowledgments

We thank Drs. Elizabeth Murphy and John Pritchard for helpful suggestions during the preparation of this manuscript.

Footnotes

  • 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.

REFERENCES

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