Differential Renal Gene Expression in Pre-hypertensive and ...

Articles in PresS. Am J Physiol Renal Physiol (March 29, 2005). doi:10.1152/ajprenal.00354.2004

FINAL ACCEPTED VERSION

F-00354-2004.R2 1

Differential Renal Gene Expression in Pre-hypertensive and Hypertensive Spontaneously Hypertensive Rats

Seubert JM1, Xu F2, Graves JP1, Collins JB3, Sieber SO3, Paules RS3, Kroetz DL2, Zeldin DC1

1

Division of Intramural Research, National Institute of Environmental Health

Sciences, Research Triangle Park, NC 27709 2

Department of Biopharmaceutical Sciences, School of Pharmacy, University of

California, San Francisco, CA 94143 3

Microarray Group, NCT, National Institute of Environmental Health Sciences,

Research Triangle Park, NC 27709

Corresponding Author:

Darryl C. Zeldin, MD National Institute of Environmental Health Sciences 111 T.W. Alexander Drive, Building 101, D236 Research Triangle Park, NC 27709 Phone: 919-541-1169

Fax: 919-541-4133

E-mail:[email protected]

Copyright © 2005 by the American Physiological Society.


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ABSTRACT Development of hypertension stems from both environmental and genetic factors wherein the kidney plays a central role. Spontaneously hypertensive rats (SHR) and the non-hypertensive 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 pre-hypertensive stage (3 weeks of age) and hypertensive stage (9 weeks of age).

Additionally, age-related

changes in gene expression patterns were examined from 3 to 9 weeks 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 NIEHS Rat v2.0 Chip which contains approximately 6700 genes. Twenty two genes were found to be differentially expressed between SHR and WKY at 3 weeks of age, and 104 genes were differentially expressed at 9 weeks 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 weeks 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.


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INTRODUCTION 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 which 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 physiologic 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 (10-12, 34, 41-43). For example, a shift from peripheral arterial resistance to arterial stiffness occurs with age (10-12, 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


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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 pre-hypertensive stage (3 weeks of age) and during the developmental phase of hypertension in the SHR (9 weeks of age).

While blood pressure is similar in SHR and

normotensive controls at 3 weeks of age, changes in glomerular function, pressure-natriuresis, and vascular structure are well documented in prehypertensive SHRs (8, 40, 46). At 9 weeks 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, vascular and renal phenotypes at each age.

A comparison of changes in the SHR

between 3 and 9 weeks 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.


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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 pre-hypertensive (3 weeks) and hypertensive (9 weeks) stages of life. Three and nine week-old SHR and WKY rats were purchased from Charles River Laboratories (Wilmington, MA).

Rats were

anesthetized with methoxyflurane, abdominal cavities were opened and kidneys were perfused with ice-cold phosphate buffered saline 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 NIEHS cDNA Rat v2.0 Chip, which contains approximately 6700 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 2000 base pairs (Incyte Genomics, Palo Alto, CA). M13 primers were used to amplify insert cDNAs from purified plasmid


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DNA in a 100µl 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 onto poly-L-lysine coated glass slides using a modified, robotic DNA arrayer (Beecher Instruments, Bethesda MD). Each total RNA sample (15-75 µg), representing 5-6 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® reverse transcriptase (Invitrogen, Carlsbad, California) and Oligo dT primer (Amersham, Piscataway, NJ).

The

fluorescently-labeled cDNAs were mixed and hybridized simultaneously to the cDNA microarray chip. Each RNA pair was hybridized to a total of 4 arrays employing a fluor reversal accomplished by labeling the control sample with Cy3 in 2 hybridizations and with Cy5 in the other 2 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. The raw pixel intensity images were analyzed using the ArraySuite v2.0 extensions of the IPLab image processing software package (Scanalytics, Fairfax, VA). This program uses methods that were developed and previously described by Chen and co-workers (5) to locate targets on the array, measure


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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 was 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 was 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 4 replicate arrays for each sample, lists of

differentially expressed genes at the 95% confidence level were created and deposited into 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 3 of the 4 replicate experiments. Any of these genes that indicated fluor bias or high variation were not considered for further analysis.

Assuming that the


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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 approximately 0.00048. Hierarchical clustering was performed using Eisen's Cluster/Treeview software package (http://rana.lbl.gov) (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 (NCBI/NIH). 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, Carlsbad, CA) 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 if P