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Physiol. Genomics 29: 169-180, 2007; doi:10.1152/physiolgenomics.00229.2006
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Received 20 October 2006; accepted in final form 5 January 2007.
Physiological Genomics 29:169-180 (2007)
1094-8341/07 $8.00 © 2007 American Physiological Society

Sex-specific regulation of gene expression in the aging monkey aorta

Hongyu Qiu 1, Bin Tian 2, Ranilo G. Resuello 3, Filipinas F. Natividad 4, Athanasios Peppas 1, You-Tang Shen 1, Dorothy E. Vatner 1, Stephen F. Vatner 1,* and Christophe Depre 1,*

1 Cardiovascular Research Institute, Department of Cell Biology and Molecular Medicine, New Jersey Medical School, Newark, New Jersey
2 Department of Biochemistry and Molecular Biology, University of Medicine and Dentistry of New Jersey (UMDNJ), New Jersey Medical School, Newark, New Jersey
3 Simian Conservation Breeding and Research Center (SICONBREC), Incorporated, Philippines
4 St. Luke's Medical Center Quezon City, Philippines


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Although increased vascular stiffness is more prominent in aging males than females, and males are more prone to vascular disease with aging, no study has investigated the genes potentially responsible for sex differences in vascular aging. We tested the hypothesis that the transcriptional adaptation to aging differs in males and females using a monkey model, which is not only physiologically and phylogenetically closer to humans than the more commonly studied rodent models but also is not afflicted with the most common forms of vascular disease that accompany the aging process in humans, e.g., atherosclerosis, hypertension, and diabetes. The transcriptional profile of the aorta was compared by high-density microarrays between young and old males or females (n = 6/group). About 600 genes were expressed differentially when comparing old versus young animals. Surprisingly, <5% of these genes were shared between males and females. Radical differences between sexes were especially apparent for genes regulating the extracellular matrix, which relates to stiffness. Aging males were also more prone than females to genes switching smooth muscle cells from the "contractile" to "secretory" phenotype. Other sex differences involved genes participating in DNA repair, stress response, and cell signaling. Therefore, major differences of gene regulation exist between males and females in vascular aging, which may underlie the physiological differences characterizing aging arteries in males and females. Furthermore, the analyses in young monkeys demonstrated differences in genes regulating vascular structure, implying that the sex differences in vascular stiffness that develop with aging are programmed at an early age.

extracellular matrix; gender; vasculature; microarray


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
VASCULAR AGING MARKEDLY INCREASES the risk of cardiovascular disease in the elderly population as a consequence of structural changes in the vascular wall resulting in increased vascular stiffness (16). Indeed, the aging vasculature and its complications of atherosclerosis, hypertension, and diabetes represent potentially the greatest causes of death and disability (16). It is also documented that the increased vascular stiffness with aging is more prominent in males than females (23). Although it could be predicted that the differences in the response of the aging vasculature between males and females would be due to differences in the regulation of gene expression, no study is available examining these sex-related genomic differences in aging vessels, even in rodents where the majority of work on vascular aging has been performed. It would be ideal to conduct such a study in humans. However, it would be difficult obtaining appropriate tissue samples from aging humans. Moreover, aging in humans is rarely not associated with other vascular diseases, e.g., atherosclerosis, diabetes, and hypertension. For the current investigation, we relied on a nonhuman primate model of aging that develops changes in vascular stiffness with aging but is not complicated by the associated vascular diseases of aging (27). A key advantage to this model, over the most commonly used models in rodents, is the phylogenetic proximity to humans, as well as the possibility to study animals in which the age differences span over decades, rather than months in rodents.

We tested the hypothesis in this monkey model that the transcriptional adaptation to aging will show sex differences. The transcriptional profile of the aorta was compared by high-density microarrays between young and old males and females. Our experiments show that the transcriptional changes with aging are radically different between males and females and that several subsets of genes playing a major role in the maintenance of the vascular structure show opposite regulation between sexes. We propose that such genomic differences might underlie the physiological differences characterizing aging arteries in males and females.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Animal model.
The animals supplied for our studies come from the same species of Philippine monkey, which is the only species of old world monkeys in the Philippines. All young monkeys were bred in captivity at the SICONBREC breeding center. The old and young monkeys came from different colonies as identified from their tattoo numbers and date of birth for bred monkeys (young).

Twelve young (6.6 ± 0.5 yr old) and twelve old (20.0 ± 0.5 yr old) monkeys (Macaca fascicularis) fed ad libitum and distributed equally between sexes were included. Young monkeys were second generation, and old monkeys were feral animals captured at the age of 5–7 yr old and kept in captivity for 12 to 15 yr (2). All animal care and protocols were reviewed and approved by the Institutional Animal Care and Use Committee of UMDNJ, New Jersey Medical School. Monkeys were chronically instrumented with aortic pressure and dimension transducers and studied conscious in the tether system. Blood samples were obtained in the fasting state for determination of plasma concentration of cholesterol, triglycerides, and glucose. Histopathology was performed on paraffin-embedded sections stained with hematoxylin and eosin.

mRNA preparation and labeling.
Total RNA was extracted from each monkey thoracic aorta sample with phenol-chloroform (6) and reverse-transcribed into double-stranded DNA (8). The DNA was subsequently transcribed into biotin-labeled synthetic antisense RNA, using the Bioarray RNA Labeling Kit (ENZO). Labeled RNA was cleaned up (Qiagen), fragmented by alkaline treatment, and hybridized to the array overnight.

DNA microarrays.
Affymetrix GeneChip Human Genome U133A 2.0, which includes a 22,277-probe set representing 14,500 characterized genes, was used. After hybridization, each array was washed, stained with streptavidin-phycoerythrin and scanned twice (Affymetrix gene Array scanner), and average intensity for each probe pair was generated. Data were first analyzed using Affymetrix Microarray Analysis Suite to assess quality of RNA and hybridization. The microarray data have been submitted to the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI; accession number: GSE6599).

Microarray data analysis.
Probe sets for which fluorescent signals were not detected in at least four samples from each group were discarded. All values from each array were normalized to the 75th percentile value of the array, which was arbitrarily set at 100. Selected probe sets were analyzed by the Student's t-test. A P < 0.05 and fold change >1.2 were considered significantly different between groups. Cluster analysis was performed with the CLUSTER program (9) using Pearson correlation as metric. Clusters and the heat map were presented by the TreeView program.

Gene ontology analysis.
Selected probe sets were mapped to entries in the NCBI Gene database using Affymetrix online information (NetAffx). Gene ontology (GO) annotations for genes were obtained from the Gene database of NCBI. For each GO term, a statistical test based on the hypergeometric distribution was used. P values were adjusted by the Bonferroni method.

Transcription factor analysis.
The transcriptional start sites for human genes were mapped by aligning RefSeq sequences from NCBI to UCSC genome sequences using BLAT (14). We retrieved genomic sequences surrounding the transcriptional start sites from –700 nt to +300 nt (1K set). Transcription factor binding sites were identified using the Match tool (13) and the TRANSFAC database (version 9.3) (30). Transcription factor binding sites that were significantly associated with upregulated and downregulated genes were identified based on a hypergeometric distribution.

Quantitative RT-PCR.
Following reverse transcription of the mRNA of interest from 50 ng of total RNA, the cDNA was used for quantitative (q) PCR (40 cycles of a 10-s step at 95°C and a 1-min step at 60°C) using the SybrGreen method on a 7700 ABI-Prizm Sequence Detector (Applied Biosystems, Foster City, CA) (7). Values are reported per cyclophilin transcript to correct for sample-to-sample RNA loading variations.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Characteristics of monkeys.
Four groups of monkeys (n = 6 per group) were included in this study: young (average 6.6 yr old) versus old (average 20 yr old) males and females. The physiological characteristics of the four groups are represented in Table 1. Body sizes, i.e., body height, body weight, and body surface area, of female monkeys were smaller than those of male monkeys. However, there were no significant differences in body weight, height, and body surface area between young and old monkeys in both sexes. Measurements of cholesterol, triglycerides, and fasting plasma glucose in the blood were not different among the four groups (Table 1). Histological analysis was performed to exclude the presence of vascular disease, such as inflammation and/or atherosclerosis. Representative examples are shown in Fig. 1. Therefore, the groups are comparable in terms of physiological, biochemical, and histological characteristics.


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  		Table 1. Body sizes and measurements of blood samples

 

Figure 1
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  		Fig. 1. Histological characteristics of the monkey aorta in the 4 groups. Photomicrographs of the thoracic aorta from young (A and C) and old (B and D) females (A and B) and males (C and D). Hematoxylin-eosin staining, magnification x10.

 
The aortic pressure-diameter relationships (PDR) in young and old monkeys during bolus injection of sodium nitroprusside and phenylephrine are shown in Fig. 2. The slope of the aortic PDR, which measures aortic stiffness, was significantly less in old male monkeys than in young male monkeys indicating a stiffer aorta in old animals (Fig. 2). Thus, for any given decrease or increase in aortic pressure, there was less of a change in aortic diameter in old monkeys compared with that in the young group. However, this difference was not observed in females (Fig. 2), demonstrating a sex difference in vascular stiffening with aging.


Figure 2
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  		Fig. 2. Sex difference in vascular stiffening with aging in the monkey. Aortic pressure and aortic diameter relationships in response to bolus injection of sodium nitroprusside and phenylephrine are plotted as percent change from baseline in young male (A) and female monkeys (B) ({circ}) and in old male and female monkeys (•). The slope of the relationship between mean aortic pressure and aortic diameter in old male monkeys (dashed line) is significantly less (P < 0.05) than that in young male monkeys (solid line), suggesting a stiffer aorta in the old male monkeys. This difference was not observed in female monkeys.

 
Aging regulates a different gene program in males and females.
Total RNA was successfully obtained from all samples and hybridized on microarrays. The hierarchical clustering was performed to group samples and genes (Fig. 3A). Using a P value < 0.05, we found a total of 672 probe sets regulated by aging when comparing old versus young animals (206 in females and 466 in males), whereas 526 probe sets were regulated by sex when males and females were compared (353 in young animals and 173 in old animals). The total number of genes corresponding to the probe sets that showed a significant (P < 0.05) regulation is indicated for each group in Fig. 3B. Similar numbers of genes were found when by the ANOVA test (not shown). We found the largest number of regulated gene products when comparing old and young males (Fig. 3B).


Figure 3
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  		Fig. 3. Hierarchical cluster analysis of the probe sets in the different groups. A: each cluster shows the total number of probe sets that were significantly (P < 0.05) regulated in each group. B: number of genes that were either upregulated or downregulated (P < 0.05) in each group. OF, old female; YF, young female; OM, old male; YM, young male.

 
The results presented above show that substantial numbers of genes are regulated in the vasculature as a response to aging and between sexes. We investigated next whether the transcriptional regulation to aging is affected by sex, i.e., whether the genes that are either upregulated or downregulated between old and young animals are the same in males and in females. Remarkably, only 1.7% (4 out of 236) of genes that are upregulated and 4.0% (15 out of 387) of the genes that are downregulated by aging are shared between males and females (Fig. 4A). The identity of these 19 genes is indicated in Fig. 4A, which show that both sexes presented a significant downregulation with aging of the mRNA encoding collagen type I, as well as a downregulation of growth factors and translation effectors.


Figure 4
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  		Fig. 4. Qualitative differences in gene expression between males and females and between young and old animals. A: sex comparison of the effects of aging on gene regulation. B: age comparison of the effects of sex on gene regulation. The identity of the genes shared between groups, when we compared males vs. females at both age points (A), or young vs. old animals in both sexes (B), is indicated.

 
Using a similar comparison, we tested how the transcriptional difference between sexes is affected by aging, i.e., whether the genes that are differently expressed between males and females are the same in young and old animals. Out of 267 genes that were upregulated and 237 genes that were downregulated when comparing females versus males, only 1.9% (5 out of 267) and 2.5% (6 out of 237), respectively, were shared between young and old animals (Fig. 4B). These 11 gene products are identified in Fig. 4B, which includes Y-linked or X-linked transcripts (such as DEAD box, Xist, the ribosomal protein S4 or the translation initiation factor 1A). Taken together, these results illustrate that the gene regulation by aging markedly differs between males and females and that the transcriptional differences between sexes are distinct when one compares young and old animals. From the total pool of regulated genes shown in Fig. 3, we identified those showing at least a twofold difference between groups (Tables 2 and 3). Table 2 shows the effects of aging, whereas Table 3 shows the effects of sex. The comparison between old males and young males provided about twice the number of genes with a ≤2-fold change than any of the three other groups. However, these results also show that multiple growth-promoting genes are expressed more in young males than females (Table 3), which suggests the possibility that the differences of stiffness and cell proliferation observed in old animals might already be programmed at an early stage.


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  		Table 2. Gene regulation (≥2-fold) with aging in female and male monkeys

 

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  		Table 3. Gene regulation (≥2-fold) with sex in young and old monkeys

 
Specific genes show a different regulation between sexes.
Analysis of the different groups was further performed by GO. The different subsets of genes found by an ontology search were sorted in each group according to their P value (Fig. 5), from the most significant upregulation (red color) to the most significant downregulation (green color). Using this analysis, we found a very significant difference between males and females during aging for genes participating in extracellular matrix (ECM) and in vascular smooth muscle cell (VSMC) phenotype (Table 4). Regarding the ECM, aging males remarkably showed a downregulation of at least eight different forms of collagen, whereas aging females showed a downregulation of only two forms (Table 4). The only exception to this global downregulation of collagen transcripts was the upregulation of the mRNA encoding collagen type VIII, an isoform specifically promoting the neointimal migration of VSMC (see DISCUSSION). Other components of the ECM, including aggrecan 1, chondroitin sulfate, fibrillin 1, and the microfibrillar-associated protein 1 also showed sex differences (Table 4). Altogether, these data demonstrate that the ECM composition, interaction, and remodeling differ between aging males and females, which can be directly responsible for different physiological properties of the vessel. In addition, important sex differences during aging were also found for genes encoding proteins involved in the phenotype of VSMC, including cytoskeleton, membrane receptors, and signaling pathways (Table 4). A regulation of multiple genes (n = 17) in the group of aging males was consistent with a switch from the "contractile" to the "migratory/secretory" phenotype that characterizes the VSMC migrating toward the neointima. However, only few of these genes (n = 6) were found to be regulated in aging females (Table 4). Especially, at least five effectors of the Ras and Rho pathways were upregulated in males but not in females, further confirming this sex difference in cell growth and dedifferentiation (Table 4).


Figure 5
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  		Fig. 5. Gene ontology analysis among the 4 groups. Subsets of genes that were significantly upregulated or downregulated in each group. The gene subsets are sorted according to P value, from the most significant upregulation (red) to the most significant downregulation (green). The gene subsets showing an opposite regulation between groups are indicated in bold.

 

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  		Table 4. Gene regulation in extracellular matrix and VSMC phenotype with aging

 
In addition to the clusters of genes involved in ECM and VSMC phenotype, other interesting differences could be observed between aging males and females. For example, whereas aging females showed a very significant upregulation of genes involved in phospho-transferase activity, the opposite pattern (i.e., a downregulation with aging) was observed in males (Fig. 5). In addition, aging males, but not aging females, showed a significant downregulation of genes involved in DNA repair (Fig. 5), whereas aging females, but not aging males, showed a significant downregulation of signal transduction activity (Fig. 5). The identity of these genes showing the most significant differences during aging by GO is presented in the Supplementary Table S1. (The online version of this article contains supplementary material.)

The same comparison by GO also showed different changes in gene expression between sexes (Fig. 5). For example, comparison of young females versus young males showed a strong downregulation of genes involved in transcriptional activity in the first group, whereas the opposite was found when comparing old females and old males (Fig. 5), which suggests a higher capacity of protein renewal in aging females. Old females also showed a much higher stress response than old males (Fig. 5). The identity of the genes showing the most significant differences by GO between sexes is presented in the Supplementary Table S2.

Validation of microarrays by qPCR.
The gene regulation shown by microarray analysis was further validated by quantitative RT-PCR (qPCR) for 10 gene products picked at random among the four comparisons. All these gene products showed a similar directional change and similar amplitude of regulation by microarrays and by qPCR (Fig. 6). When plotting the fold changes obtained for these 10 products by microarrays and by qPCR, the correlation coefficient between both techniques was R = 0.89 (P < 0.01).


Figure 6
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  		Fig. 6. Validation of the microarrays by quantitative (q) PCR. Fold changes measured by both techniques for 10 gene products picked at random among the 4 groups. DDX3Y, Y-linked DEAD box polypeptide 3; PTN, pleiotrophin; CKS2, CDC28 protein kinase regulatory subunit 2; PER2, period homolog 2; DUSP, dual specificity phosphatase; eEF-1A2, eukaryotic elongation factor-1A2; FGF-1, fibroblast growth factor-1; IGF-2, insulin-like growth factor-2; AGC-1, aggrecan-1.

 
Analysis of transcription factor binding sites.
We analyzed next whether the transcriptional regulation described above might correspond to specific transcription factors. The candidate transcriptional regulators were determined by searching the presence of consensus binding sequences for trans-activating elements within a distance of 1 kb from transcriptional initiation. In the Supplementary Table S3, we show the identity of the transcription factors corresponding to the genes that are regulated with a P < 0.01, as well as the number of genes potentially regulated by each specific factor. Again, marked differences were found when comparing groups, reflecting the marked differences in the identity of the regulated gene products.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
Vascular aging markedly increases the risk of cardiovascular disease in the elderly population as a consequence of structural changes in the vascular wall, resulting in increased vascular stiffness (16). These structural changes, which have been characterized both in rodents and in larger mammals, also correlate with observations in patients, where the aging artery is characterized by intimal thickening, VSMC migration, and elastin fragmentation (16). A better understanding of this process requires determining whether the global genomic adaptation of the vascular wall to aging makes this vessel more vulnerable to other forms of vascular disease, such as atherosclerosis, hypertension, diabetes, and/or endothelial dysfunction. In particular, there is no study that has investigated potential sex differences in vascular aging. Such differences are likely, however, considering that males are more prone than females to atherosclerosis and hypertension (4, 31). Our goal was to test whether these well recognized physiological differences result from genomic variations between both sexes in the adaptation to aging. Our findings show that the changes in gene expression during aging differ >90% between males and females. Gene changes between young males and females differ from the changes found when we compared the sexes in old animals. These qualitative differences correlate with the regulation of different sets of transcription factors. Most importantly, GO analysis shows that specific subsets of genes, particularly those involved in ECM and VSMC phenotype, are differently regulated between groups. In addition, sex differences in the expression of genes linked to sex chromosomes suggest that the different susceptibility of males and females to aging may begin at an early age. We propose that these differences at the gene level might underlie the physiological differences in vasoreactivity described between males and females in response to aging (4).

Although previous studies addressed the genomic adaptation to vascular aging, it was not in a context of sex difference. Most of the published work has been performed in rodent models, where vascular aging is characterized by an increased migration of VSMCs to the neointima. At the genomic level, these morphological characteristics are accompanied by a shift of VSMCs from the contractile to the migratory phenotype, which includes a change in expression of myosin isoforms and proteins of the cytoskeleton (15). These alterations resemble those found in the pathogenesis of atherosclerosis or in other forms of vascular injury. Our study shows that aging-related changes in gene expression are sex dependent and include ECM composition, VSMC phenotype, cell signaling pathways, resistance to apoptosis, metabolism, protein synthesis, and transcription factors.

An observation that is specific for males is the global downregulation with aging of genes involved in the synthesis of the ECM and in particular of different forms of collagen (Table 2). In addition, aging males but not females showed a decrease in collagen type III. Interestingly, collagen type III decreases the size of collagen bundles and thereby increases vascular elasticity (11). Therefore, a decreased expression of collagen type III can participate in the increased stiffness that characterizes the aging aorta (23). An interesting observation from our study that directly relates to the mechanism of vascular remodeling is the upregulation in aging males of the transcript encoding collagen type VIII (Table 3). That specific collagen type, which is upregulated in response to vascular injury (24), promotes VSMC migration (1). The upregulation of this transcript together with the downregulation of other isoforms in aging males again supports the notion that this group is more susceptible to neointimal proliferation, VSMC migration, and potentially atherosclerosis.

Cell signaling pathways related to the VSMC phenotype show a sex difference in our study. For example, comparison of old males versus young males shows an upregulation of multiple genes involved in cell communication that include several Rho GTPases, in particular ArhGAP-1, which signal through the MAPK pathway (Table 4). These gene products are particularly important in the migratory and proliferative activity of VSMC (12). The fact that these products were not upregulated in females correlates with the observation that aortic VSMC from male rats show a higher capacity of cell division, shorter cell cycle, and faster migration than corresponding female cells (3). Another observation specifically made in males is the downregulation with aging of genes involved in DNA repair. This observation, which suggests a higher susceptibility to apoptosis, is supported by our previous observation in the same model that aortas from male monkeys show a higher percentage of TUNEL-positive apoptotic cells compared with young animals (2).

M. fascicularis is a primate with stable karyotype, and its genome is highly conserved with humans, ~97% in the coding region and ~95% in the 3'-untranslated region for Macaca mulatta (19), the closest primate to M. fascicularis for which the genome sequence is available. Therefore, we relied on human DNA microarrays to examine mRNA expression of monkey genes. By using our analysis system as stated in MATERIALS AND METHODS, we detected 14,644 probe sets. Despite the extensive similarity in genomic sequences, it is expected that some sequences may differ, which can affect the detection of mRNA gene expression. Thus, some of the differentially regulated genes may be elusive in this study, and the number of genes that have sex or age differences in expression are likely larger than reported here. In this sense, the genes that we selected in this study are presumably highly conserved among primates (Supplemental Figure S1).

We used the t-test to select genes instead of statistical approaches based on false discovery rates (25). This choice is primarily based on the fact that the difference of gene expression between individuals in a natural population, as opposed to lab animals, can be high (29). This high variation would require a much larger number of samples for stringent biostatistical methods. Thus, our approach represents a balance between the sample size and the false discovery rate.

Sex differences in the genomic response to cellular stress have already been demonstrated in other tissues than the vessel, such as in the liver during ethanol intoxication (26), in blood cells exposed to benzene (10), or in the heart after pressure overload (28). This variability likely affects the disease propensity of the corresponding organs between males and females. For example, all other risk factors being equal, male patients are much more prone than matching females to aplastic anemia (21) and cardiac remodeling (18), whereas the opposite is true for alcohol-induced cirrhosis (17). Even in the absence of disease, several organs, in particular the gonads, liver, kidney, and brain, show sexual dimorphism (22).

One potential mechanism of sex-specific gene regulation relates to sex-linked chromosomes. Several genes encoded by sex-linked chromosomes showed a very significant difference between genders already in young animals (Fig. 4B). This difference may be particularly important for genes involved in protein synthesis, such as the Y-linked translational initiation factor 1 and ribosomal protein S4 (Fig. 4B), because they suggest that the genomic changes described here will be accompanied by a similar adaptation of translation. Similarly, the Y-linked DEAD box polypeptide 3 (DDX3) has not been characterized in the vasculature before but is known to participate in cellular dedifferentiation and proliferation in tumors (5). Interestingly, these genes have a homolog on the X chromosome. Males and females have the same level of transcript expression from the X allele because one of the X chromosomes is inactivated in females, which provides a mechanism of dosage equivalence between sexes. The fact that the Xist mRNA, which performs this dosage equivalence, is upregulated ~30-fold in females (Fig. 4B), shows that the X inactivation is correctly performed. This conclusion is further supported by the data presented in Fig. 4B, where the sex difference of the X-linked DDX3 gene is in the 1.0–1.5 range, whereas the difference of its Y counterpart is in the 10.0–20.0 range. Therefore, when combining the dosage from both the X and Y alleles, males have a higher dosage for these genes than females. In the case of DDX3, this sex difference in expression is up to 20-fold (Fig. 4B), which very likely induces physiological consequences.

It is interesting to speculate that the sex differences in vascular stiffness observed in old animals are already programmed at an early stage in life. This hypothesis is supported by the observation that multiple growth-promoting genes, especially those related to sex-linked chromosomes, already show a different expression between sexes in young animals. Therefore, the different susceptibility of both sexes to vascular remodeling and to the risk of vascular disease may begin at an early age.

In addition to sex-linked chromosomes, a sex-specific regulation of gene expression is performed by sex hormones. Sex steroids affect the trans-activation of multiple genes, including several subsets that are regulated in our study. For example, estradiol decreases collagen deposition, whereas it increases the expression of elastin and fibrillin 1 transcripts (19), in agreement with the regulation of the ECM presented in Table 4. Also in agreement with our results (Table 4), estradiol decreases the growth capacity of VSMC through the regulation of complementary signaling pathways (20).

Our study shows that the genomic adaptation to vascular aging involves not only the genes involved in ECM composition and VSMC differentiation and migration, but also many other categories of genes participating in intracellular functions, such as cell signaling, DNA repair, metabolism, and protein synthesis. Our study also illustrates that most of the changes in gene expression with aging differ between males and females and correspond to different sets of transcription factors. Indeed, <5% of the 600 genes that were regulated by aging were observed in both old males and females. GO analysis also shows that specific subsets of genes are regulated differently between sexes, especially the genes participating in ECM composition and VSMC phenotype. We therefore propose that these transcriptional differences may underlie the different physiological properties of aging arteries between males and females, as well as their different susceptibility to vascular complications, such as hypertension or atherosclerosis. Furthermore, the analyses in young monkeys demonstrated major differences in genes regulating vascular structure, implying that the sex differences in vascular stiffness that develop with aging are programmed at an early age.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This work was supported by National Institute of Health Grants HL-69020, AG-23137, AG-28854, AG-14121, HL-33107, HL-59139, HL-69752, and HL-72863.


   ACKNOWLEDGMENTS
 
We would like to thank Ijen Yeh and Ji Yeon Park for their contributions.


   FOOTNOTES
 
Address for reprint requests and other correspondence: S. F. Vatner, Cardiovascular Research Inst., Dept. of Cell Biology and Molecular Medicine, Univ. of Medicine and Dentistry of New Jersey, New Jersey Medical School, 185 South Orange Ave., MSB G-609, Newark, NJ 07103 (e-mail: vatnersf{at}umdnj.edu).

Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

* S. F. Vatner and C. Depre are co-senior authors. Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

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