ALTERED BETA2 ADRENERGIC RECEPTOR GENE EXPRESSION IN HUMAN CLINICAL HYPERTENSION (original) (raw)

. Author manuscript; available in PMC: 2010 Jul 1.

Published in final edited form as: Biol Res Nurs. 2009 Mar 1;11(1):17–26. doi: 10.1177/1099800409332538

Abstract

Objectives

The beta 2-adrenergic receptor is involved in mediating vasodilatation via neurohumoral and sympathetic nervous system pathways. Alterations in beta 2-adrenergic receptor gene expression (mRNA transcription) may contribute to the hypertensive phenotype. Human gene expression in clinical phenotypes remains largely unexplored due to ethical constraints involved in obtaining human tissue. We devised a method to obtain normally discarded internal mammary artery tissue from coronary artery bypass graft patients. We then investigated differences in hypertensive and normotensive subjects’ beta 2-adrenergic receptor gene expression in this tissue.

Methods

We collected arterial tissue samples from 46 coronary artery bypass patients in a surgical setting. Using 41 of the samples, we performed TaqMan® RT-PCR and used the delta delta cycle threshold (ΔΔCt) relative quantitation method for determination of fold-differences in gene expression between normotensive and hypertensive subjects. The beta 2-adrenergic receptor target was normalized to glyceraldehyde-phosphate dehydrogenase.

Results

Subjects with hypertension had significantly less expressed beta 2-adrenoceptor gene (2.76-fold, p < .05) compared to normotensive subjects. After Bonferroni correction, gene expression did not differ by race, gender, type/dose of beta-blocker prescribed, positive family history of hypertension, or diagnosis of diabetes mellitus type 2.

Conclusions

These data support the possibility of a molecular basis for impaired adrenoceptor-mediated vascular tone in hypertension. Modification and extension of this research is required.

Keywords: Beta 2-adrenergic receptor, gene expression, transcription, hypertension, coronary artery

Adrenergic receptors (AR) influence blood pressure (BP) via neurohumoral and sympathetic nervous system regulation. Alterations in ARs at cellular and molecular levels may lead to hypertension (HTN) (Hindorf et al., 2005; Small, McGraw, & Liggett, 2003). The beta 2-adrenergic receptor (ARB2) mediates vasodilation in vascular smooth muscle as a target receptor for epinephrine and norepinephrine; when ARB_2_ protein function is impaired, HTN may ensue (Monopoli, Conti, Forlani, & Ognini, 1993).

Early protein-expression work showed that ARB2 density was positively correlated with mean arterial BP in lymphocytes of borderline and essential hypertensive subjects (Brodde, Daul, O’Hara, Wang, & Bock, 1985) and in mononuclear cells across a wide range of subjects with normal to increased BPs (Middeke, Remien, & Holzgreve, 1984). These protein expression studies added support to the hypothesis that impaired ARB2 relaxation was involved in the HTN disease process; however, a question remained as to whether or not genetics had an influence on the ARB2 density. Accordingly, Michel, Galal, Stoermer, Bock, and Brodde (1989) examined lymphocyte ARB2 density in children with positive and negative parental histories of HTN. They found no difference in receptor density between groups, implying nongenetic influences on ARB2 mechanisms in HTN. In more recent years, however, the genomic revolution has allowed more robust explorations into the possible molecular mechanisms in ARB2 density and HTN through gene association studies. Both positive and negative associations of ARB2 gene variants (polymorphisms) with HTN have led to inconclusive evidence for alterations at the DNA level impacting the development of HTN (see reviews by Hantow et al., 2006, and Small, McGraw, & Liggett, 2003).

The following approach can be applied to the study of AR involvement in disease: the genetic code (DNA) provides the template for transcription of messenger RNA (mRNA), which is then translated into protein (protein being the actual receptor, in this case). Transcriptional activity is generally thought to be influenced by environmental factors. In the case of the ARB2 gene and HTN, for example, dietary sodium chloride intake is a mediator of ARB2 gene expression in humans (Feldman, 2000). (An interesting side note: there is new evidence for possible genetic determinants of gene-expression traits that may change the way we view the interface between genetics and genomics in the future [Emilsson, Thorleifsson, Zhang, Leonardson, Zink, & Zhu, 2008].) We hypothesize that molecular mechanisms like gene transcription and regulation, as opposed to genetic architecture (i.e., DNA sequence) or protein expression (i.e., receptor density/affinity), may be important for understanding differences in ARB2 function between hypertensive and normotensive individuals.

Little is known about the genetic expression of ARs in human arterial tissue in the context of clinical HTN. Only one study to date has examined gene expression (RNA transcription) of AR between persons with and without HTN; however, the investigators used blood-based RNA detection and focused on the alpha-1A subtype (Veglio et al., 2001). It is generally accepted that receptors of any kind have different properties and expression levels in various tissue and cell types, making comparisons between blood and tissue studies suboptimal. It is also widely accepted that diseased tissue (rather than blood cells, lymphocytes or mononuclear cells, for example) has the most potential for revealing disease mechanisms that are not primarily hematologic, largely due to the belief that the circulating transcriptome has the potential for interaction with many confounding substances and/or mediators within the blood, which is not a factor within tissue. For the aims of our study described here, arterial tissue is one appropriate source for studying HTN; however, tissue is the most difficult resource to safely and ethically obtain in human clinical research. As such, there is a knowledge gap regarding if and how ARB2 mRNA is expressed in human arterial tissue in the context of clinical HTN.

Our primary aim in this study was to explore ARB2 gene expression (mRNA levels) in the clinical phenotype of HTN by examining human internal mammary artery samples in coronary artery bypass patients with and without HTN. Human coronary artery contains equimolar amounts of ARB1 and ARB2 protein subtypes, with ARB2 specifically mediating vasodilatation in vascular smooth muscle types and, consequently, affecting BP (Monopoli et al., 1993). Ferro, Kaumann, and Brown (1993) reported predominantly ARB2-mediated relaxation specifically in human internal mammary artery exposed to both epinephrine and norepinephrine in vitro, providing a basis for harvesting this tissue type. We utilized genomic techniques with human samples in order to address HTN in the real-world context of disease, emphasizing gene expression as a possible key to differences in ARB2 mechanisms in human HTN.

Materials and Methods

Subjects

We obtained Institutional Review Board (IRB) approval from the University of Florida and the Malcom Randall Veterans Administration (VA) Subcommittee for Clinical Investigations. English-speaking patients between the ages of 21 and 70 who were scheduled for coronary artery bypass graft (CABG) surgery involving the internal mammary artery (typical for bypass of the left anterior descending artery) were recruited by purposeful sampling. All participants gave written informed consent at least 1 day prior to their surgical intervention to allow them the opportunity to withdraw from the study (IRB requirement). We enrolled 51 participants from north central Florida between August 2004 and July 2005. Surgical scheduling changes prevented collection of samples from 5 participants, while an additional 5 were excluded from analyses due to insufficient RNA yields and/or undetermined readings in the TaqMan® real-time polymerase chain reaction (RT-PCR) gene expression detection system. The remaining 41 participants (17 normotensive and 24 hypertensive subjects) were included in data analyses.

Study design

This case-control study involved collection of normally discarded surgical remnants of internal mammary artery from patients undergoing scheduled CABG surgery. We preserved and stored the collected arterial remnants for batch analysis. Following tissue processing and RNA extraction, we performed TaqMan® RT-PCR and used the delta delta cycle threshold (ΔΔCt) relative quantitation method for determination of fold-differences in gene expression between groups, normalized to glyceraldehyde phosphate dehydrogenase (GAPDH) gene expression levels. This analysis technique is an optimal exploratory starting point for detecting group differences; further investigations into mRNA copy number (discrete amounts of mRNA) and protein expression are typical next steps once differences are established with these methods.

Data collection

Arterial tissue samples were collected in the surgical suites of one community and one VA hospital. The surgical remnants were cleaned of excess fasciae, sectioned, and placed in a microtubule of RNAlater™ solution (Qiagen, Valencia, CA). The samples were transported on ice to the UF Center for Pharmacogenomics Core Laboratory, where they were stored according to RNAlater™ instructions until batch analysis. Participants were grouped by diagnosis of HTN or normotension (NT) using the JNC VII criteria (Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure, 2003). Participants were considered hypertensive if they were diagnosed by a practitioner (either by self-report or medical record documentation) or had medical-record documentation of three consecutive BP readings of 140/90 mmHg or greater (with at two of the instances to be at least 3 days apart). Participants were considered to be normotensive if both criteria were absent. Of the participants with documented normal BPs and no documentation of diagnosis of HTN, 7 were taking beta-blockers (indicated for reduction of heart rate, treatment of arrhythmias, or to facilitate overall risk reduction in coronary heart disease, per treatment guidelines [Finnish Medical Society Duodecim, 2006]) and were placed into the NT group. Participants’ beta-blocker usage, type, and dose at the time of surgery were obtained from their medical charts. Positive family history of HTN was determined by asking participants to answer “yes” or “no” to having a first-degree blood relative (parent and/or sibling) diagnosed with HTN.

RNA isolation and reverse-transcription

We treated all surfaces and tools with 0.1% solution of diethylpyrocarbonate (DEPC) to water for RNA laboratory work (Applied Biosystems, 2008). Tissues were removed from RNALater™ solution, gently blotted to remove excess solution, weighed, cut into smaller pieces and transferred to a ceramic mortar. After adding liquid nitrogen, we ground the frozen samples into a fine powder with a ceramic pestle. We then combined the powdered tissue with 500 microliters of proprietary Lysis/Binding solution from the RNAqueous Kit® (Ambion, Inc., Austin, TX) and homogenized the slush with a PowerGen 125® electric rotor-stator homogenizer (Fisher Scientific, Pittsburgh, PA). After a 30-s centrifugation to remove large debris, we removed the supernatant and extracted the RNA with the RNAqueous Kit®, according to the manufacturer’s instructions. We concentrated the seven samples that had concentrations of less than 10 ng/μL in a refrigerated Centrivap Console® speed-vacuum (Labconco, Kansas City, MO). All RNA optic density 260/280 ratios were between 1.7 and 2.10. The quality of 18s and 28s peaks generated by a 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA) was consistent across all samples, indicating little RNA degradation. We reverse-transcribed samples with the cDNA Archive Kit (Applied Biosystems, Foster City, CA) per manufacturer’s instructions and stored them at −20°C.

Real-time polymerase chain reaction

We prepared 20 μL reactions for singleplex RT-PCR with the ABI PRISM® 7900system (Applied Biosystems, Foster City, CA). Glyceraldehyde-phosphate dehydrogenase (GAPDH) was used as the housekeeping, or normalizer, gene. We used the following ABI assays: ARB2 #Hs00240532_s1 and GAPDH #4310884E (catalog number). For each reaction, we prepared 10.0 μL of TaqMan® Universal PCR Master Mix (2X) with AmpErase® UNG with 1.0 μL of each respective TaqMan® Gene Expression Assay on Demand (20X). We singleplexed triplicate samples in the TaqMan® systemwith PCR conditions of 50°C for 2 min and 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. We processed and analyzed fluorescence data with the ABI PRISM® Sequence Detection Software® (Applied Biosystems, Foster City, CA and expressed results as cycle threshold, or Ct (number of cycles needed to generate a fluorescent signal above a predetermined threshold), as determined by the ABI 7900 software. The software determined the baseline automatically by assessing the normalized fluorescence signal versus cycle data per plate. From this baseline, we obtained each sample’s Ct value.

Calculations for relative gene expression and selection of calibrator

We imported all gene expression data into Microsoft Excel (Microsoft Corporation, Redmond, WA) for relative gene expression calculations. As the expected range of gene expression for the ARB2 gene target in human internal mammary artery tissue is unreported, we considered all measured values valid; therefore, we removed no outliers. We used mean triplicate Ct values to calculate the following: a) ΔCt = mean target Ct − mean endogenous control (GAPDH) Ct; b) ΔΔCt = ΔCtsample x − ΔCtcalibrator; c) 2−ΔΔCt, which is the normalized gene expression relative to the calibrator. The chosen calibrator was a normotensive, Caucasian male who was not taking any medications and reported no other cardiac or comorbid diagnoses. His body mass index (BMI) of 30.9 kg/m2 was comparable to the sample mean BMI of 29.0 kg/m2. He also reported not having ever been a smoker and did not drink or exercise.

The determination of fold-difference between groups is expressed as a ratio of the 2−ΔΔCt medians for the groups compared. There is an inverse relationship between the Ct value and gene expression. This same principle applies even after normalization. All fold-difference data were calculated and interpreted in this fashion.

Statistical analyses

We analyzed data using Microsoft Excel and SPSS version 14 (SPSS Inc., Chicago, IL). We used descriptive statistics to summarize demographic variables and we report normalized fold-differences (described previously) and significance values. To evaluate differences in gene expression of ARB2 by diagnosis, we used the nonparametric Mann-Whitney U test. All hypotheses were two-tailed. We set alpha at .05 for the first statistical comparison by diagnosis and Bonferroni corrected additional tests setting alpha at .008. We categorized the beta-blocker variable by dose for metoprolol tartrate (refer to Table 1). One subject was taking Labetalol 300 mg/day. This dose was found to be equivalent to 100–200 mg/day of metoprolol (Lacy, Armstrong, Golman, & Lance, 2005); therefore, we placed this subject in the “greater than 50 mg twice daily” category for analyses. To evaluate the efficacy of the housekeeping gene, GAPDH, we performed nonparametric Spearman’s rho to test correlations between average triplicate Ct values for GAPDH and the ARB2 targets. In addition, we used Student’s t-test to evaluate variability of the normally distributed GAPDH expression by diagnosis.

Table 1.

Subject Demographics

Results

Subjects

Table 1 shows a summary of the demographic data. We report gene expression data from 17 (41.5%) normotensive and 24 (58.5%) hypertensive subjects whose mean age was 57.1 years. A majority of our subjects were Caucasian, non-Hispanic males. Hypertensive and normotensive subjects did not significantly differ by age, race, anthropomorphic variables, family history, or type of surgery facility (Table 1). Collectively, the demographic data suggest that this sample is moderately representative of the population of bypass patients who undergo surgery in university hospital and Veterans Administration settings (i.e., primarily Caucasian males in their midfifties, overweight or obese [mean BMI of at least 29], most with HTN and diabetes); however, it may not be representative of the entire population of people with coronary artery disease or essential hypertension.

ARB2 gene expression by diagnosis

Data are expressed as a ratio of the 2−ΔΔCt medians for the groups compared. Median 2−ΔΔCt and standard deviation values are presented in Table 2. ARB2 gene expression was significantly lower in hypertensive subjects (n = 24) compared to normotensive subjects (n = 17; 2.76-fold, p = .035). The 2.76-fold difference was determined by a ratio of hypertensive to normotensive 2−Δ ΔCt values (0.69/0.25). The higher 2−ΔΔCt values for hypertensive subjects indicates a lower level of gene expression because it took more TaqMan amplification cycles to detect the mRNA (i.e., there was less mRNA present) than for the normotensive subjects. Though the data could also be interpreted as normotensive subjects having 0.36-fold higher gene expression level than hypertensive subjects (0.25/0.69), we chose to interpret the data in the former fashion as we are interested in the expression level in HTN as it compares to NT.

Table 2.

Median 2−ΔΔCt and Standard Deviation Values by Cohort

Models of cardiovascular reactivity (Anderson, McNeilly, & Myers, 1992), studies of pharmacologic response (Jennings & Parsons, 1976) and gene-association studies (Wood, 2002) support differential adrenergically mediated processes for HTN in different racial cohorts. For this reason, we removed self-reported African American subjects from the analysis and determined gene expression differences in Caucasians. In this analysis, participants who self-reported both Caucasian and Native American (n = 3) were combined with the White/Caucasian group. As shown in Table 2, ARB2 gene expression was significantly lower in hypertensive Caucasians (n = 17) compared with normotensive Caucasians (n = 15; 4.86-fold, p = .008, Bonferroni corrected).

Posthoc analyses for reclassified diagnosis

To determine if the 7 normotensive subjects who were taking beta-blockers had any effect on the analyses, we removed these subjects from the normotensive group and repeated the aforementioned analyses. There was only one change in the significance of findings as a result of this action: the fold-difference in ARB2 gene expression between Caucasian HTN and Caucasian NT groups was no longer significant after Bonferroni correction (p = .02); however, it remained statistically significant in the comparison of all normotensive subjects with all hypertensive subjects (p < .008). The shortcoming of this approach is that removing 7 subjects from the normotensive group reduces the power, likely explaining the change in significance for the one test. However, the outcome from this posthoc analysis provides some evidence that beta-blocker usage in the NT group did not seriously deteriorate the construct validity of the NT classification variable.

ARB2 and covariates

Having access to valuable clinical data on the subjects’ medical and personal history allowed us to compare diagnosis groups’ target expression differences by gender, diagnosis of type 2 diabetes mellitus, and positive family history of HTN, with the goal of evaluating potential for confounding. With further Bonferroni correction for multiple tests, no significant differences were found in target genes’ expression for these variables (data not shown). Of note, confounding by beta-blocker use was theoretically not expected, as the beta-blocker usage for our subjects was limited to ARB1-acting medications. It has been reported that metoprolol treatment increases ARB1 but not ARB2 protein expression in the human heart (Brodde, Daul, & Michel, 1990). In our data, beta-blocker usage was not a significant confounder statistically (data not shown). The lack of significant findings may indicate that these are not confounding variables with regard to gene expression; however the limitation of reduced power and the conservatism of Bonferroni correction also need to be considered.

Normalization with GAPDH

Review of the literature revealed support for the use of GAPDH as a normalizer in similar tissue types (Wang & Brown, 2001; Peuster, Fink, Reckers, Beerbaum, & von Schnakenburg, 2004). The triplicate GAPDH Ct values had a mean and standard deviation of 28.27 ± 2.99 and median of 28.23 with a range of 22.24 – 35.33. GAPDH gene expression (average triplicate Ct) was not correlated with ARB2 gene expression (R = 0.23, p = .142) but was significantly correlated with diagnosis (t = −2.61, df = 39, p < .05).

Discussion

Our data provide preliminary support for a possible molecular mechanism contributing to impaired vasorelaxation in HTN via downregulation of ARB2 gene expression. We have been unable to find other reports of human arterial tissue ARB2 gene expression for true comparison of findings. However, gene-delivery research has provided findings that are consistent with ARB2 gene dysregulation in HTN. Gaballa and colleagues (1998) reported enhanced ARB2_-_mediated vasorelaxation in de-endothelialized rat carotid arteries after adenovirally mediated delivery of the ARB2 gene. Similarly, overexpression of ARB2 in systemic hypertensive rats and (normotensive) Wistar-Kyoto rats enhanced ARB2-mediated vasorelaxation in both rat strains, with a lessened vasodilatory effect in the hypertensive strain (Iaccarino et al., 2002). The authors concluded that this vasorelaxation was directly related to ARB2 signaling dysfunction and not endothelium-dependent nitric oxide metabolism. Our data support a potential molecular mechanism for these findings. Based on our results, we infer that a contributing mechanism for HTN could be reduced vasodilation due to the ARB2 gene being downregulated.

To take our findings one step further, we would also expect to see reductions in ARB2 protein levels in HTN. Brodde et al. (1985) report, however, that protein expression of ARB2 was increased in their study of HTN. Some possible explanations involve the fact that the AR system is part of a physiologic feedback loop. The timing of our measurement of the ARB2 mRNA (and similarly, the timing of Brodde and colleague’s ARB2 protein measurements) within the negative feedback system makes interpretations difficult. First, the Ct values obtained with TaqMan RT-PCR represent only steady-state mRNA levels (Bustin, 2002), limiting our interpretations of the suspected effects of these transcription levels on posttranscriptional processes. We could infer that the system is downregulating ARB2 gene expression because ample (increased) ARB2 protein has already been produced to thwart the increase in BP. Additionally, the increased ARB2 protein expression seen by Brodde et al. could imply that the body is attempting to invoke vasodilation via the ARB2 mechanism because of the hypertensive (vasoconstrictive) state where there was already impairment in ARB2 gene expression. The complexity of the negative feedback loop in the neurohumoral model of HTN thus makes it difficult to determine cause and effect but does provide an explanation for how these two different results (decreased gene expression but increased protein expression) can coexist. Furthermore, our investigation and the work of Brodde and colleagues use different cell types, which are not comparable. Additionally, there is compelling evidence that lymphocyte expression of G-protein-coupled receptor kinase 2 (GRK2; also known as beta adrenergic receptor kinase, or BARK) may be responsible for the impairment of ARB2 responsiveness in human (Gros, Benovic, Tan, & Feldman, 1997; Gros et al., 1999) and animal (Gros et al, 2000) forms of HTN. Targeting ARB2 desensitizes the receptor to overstimulation by epinephrine (Pippig et al., 1993; Pitcher, Lohse, Codina, Caron, & Lefkowitz, 1992). These data suggest that mediators other than ARB2 mRNA alone can affect the ARB2 signaling complex and lead to impaired vasodilation.

These points highlight the complexity of the biological and physiological influences on HTN as well as the difficulty of accurately capturing meaningful data in a constantly changing physiological system. The impact of gene expression in this complex system cannot be truly established unless future studies measure mRNA and protein at the same time points from the same biological source and account for the other cofactors involved in signaling of ARB2. As none of the studies discussed are sufficient to explain cause and effect and used different species, methods, and comparisons, interpretations are necessarily limited.

As expected, group differences in ARB2 gene expression remained significant when comparing Caucasian subjects by diagnosis, as they made up a majority of the sample. This finding is consistent with prior observations of impaired ARB2-mediated vasodilation in Caucasians (Anderson, McNeilly, & Meyers 1992; Naslund, Silberstein, Merrell, Nadeau, & Wood, 1990).

Regarding expression normalization, GAPDH gene expression was not significantly correlated with expression of the ARB2 gene (a desired result indicating normalization with this gene is appropriate). The significant correlation of GAPDH gene expression with diagnosis of HTN weakens our confidence in GAPDH being invariably expressed among all samples. The literature regarding normalization with GAPDH is varied; its use for normalization with human arterial tissue has not been widely studied. Our group consulted with Ambion representatives, in addition to the literature, which led to our choice of GAPDH as the housekeeping/reference gene for this study. The significant correlation we found between GAPDH and HTN limits our study. For future investigations of this nature, we acknowledge the recommended use of multiple normalization genes (Vandesompele et al., 2002) or endogenous control gene panels for thorough assessment of normalization genes’ performance in the tissue of interest.

Limitations

There were a number of limitations in this investigation. We had limited power to detect differences between groups. We performed an a posteriori power analysis to generate Cohen’s d effect sizes for our aims. These values were then used to calculate the number of subjects needed in each group to achieve 80% power (see Table 3). This table may be useful to others interested in determining sample and effect sizes for similar work. Noninclusion of emergent bypass cases creates a sampling bias. Another potential limitation concerns construct validity of the ”normotension” diagnosis, as some subjects taking antihypertension medications for indications other than HTN were considered normotensive. Other medications used for treatment of HTN (diuretics, ACE inhibitors and calcium channel blockers) may have been prescribed for non-HTN indications like heart failure, diabetic nephropathy, or peripheral vascular disease. In determining diagnosis status for this study, subjects were categorized as hypertensive based on practitioner diagnosis and/or actual BP values that met the JNC VII criteria. Additionally, those considered “normotensive” at the time of the study could develop HTN in the future; thus, their biophysiologic mechanisms could be different from those who will never develop HTN, and likely represent a different phenotype of pre-clinical HTN. Finally, at the outset of designing this study, we anticipated the potential confounding of transcription levels by the presence of pharmacologic beta-blockade and the diagnosis of type 2 diabetes (Bengtsson et al., 2001), however, our data do not support this.

Table 3.

Effect Sizes and Numbers Needed to Power Future Studies

Conclusions

Genetics and genomics hold promise as means by which to tease out the pathobiological mechanisms and risk factors contributing to the widespread disease of HTN. With the present study, we demonstrated that our human-tissue collection methodology was feasible and shows potential for use in future research. The ARB2 gene had lower expression in hypertensive than normotensive subjects by almost 3-fold, a significant difference that supports a potential molecular basis for ARB2 dysregulation in the neurohumoral model of HTN. The significance of this finding is tempered by the normalization issues discussed above. Other factors not studied (for example, transcription factors, AR protein levels or interactions with regulatory regions and other proteins) may contribute to expression differences in the ARB2 gene and should be studied in the future. Due to the limited amount of harvested tissue and the exploratory nature of this study, we did not aim to determine ARB2 protein levels. It is currently accepted, however, that mRNA levels do not correlate with subsequent protein levels, despite mRNA’s primary role in translating proteins. Despite lack of protein data for functional correlation, these data can inform whether or not mRNA levels differ by disease state in the actual tissue.

Research of this nature--using human samples in the context of clinical phenotypes--is imperative to future translational studies to refine our understanding of genetic mechanisms of complex disease and identify risk factors with a significant genetic component. Modified replication of this research is needed to validate findings. Modifications should include further optimizing normalization in human arterial tissue and minimizing the impact of potential confounders.

Overall, this approach can be informative of disease mechanisms at the genomic level, providing information that can lead to translation of clinical detection for patient-specific genomic markers in the future. This method is not likely to directly influence practice, as removing arterial tissue during bypass surgery and analyzing gene expression in this fashion would not be amenable to the current health care setting. Rather, investigating gene expression within tissue (and possibly blood) may provide sound support for molecular-based alterations in the hypertensive disease process.

BP measurements, alone, do not paint a complete picture of the hypertensive mechanism for a particular individual and, as such, treatment modalities are often a trial-and-error approach to reducing and maintaining BP. The state of the science is moving toward personalized medicine in which clinicians will be able to tailor treatments to improve results and reduce side-effects and drug interactions. If gene-expression impairments for HTN are detectable and reproducible in the clinical population, future screening and treatment for HTN could be positively affected. The practice of screening could be modified to add molecular screening, which is likely to be more informative than BP and risk factors alone. Extension of this research into more detailed transcriptional and posttranscriptional mechanisms could potentially support gene therapy involving ARB2 gene mechanisms in human populations. Further research could elucidate how environmental variables affect ARB2 gene expression, how such factors correlate with BP phenotypes, and how ARB2 gene expression responds to different treatment modalities.

Acknowledgments

The authors wish to gratefully acknowledge Ashley Tuttle, BSN, for her assistance with collection of family history data for this study. This study was supported by the NIH National Institute of Nursing Research Ruth L. Kirschstein National Research Service Award (#1F31NR009148). Partial support for this project was received from the American Heart Association (#01415124B) and a small research grant from Sigma Theta Tau Nursing International, Alpha Theta Chapter. Shands Hospital and the Malcom Randall Veterans Administration Medical Center, Gainesville, Florida, provided resource and facility support. Partial support for this manuscript was provided by NIH grant 5T32 AG00029-30.

Footnotes

Note

Biological Research for Nursing editorial board member Sandra K. Hanneman, PhD, RN, FAAN, served as guest editor for this manuscript throughout the review process.

Contributor Information

Jennifer R. Dungan, Email: jennifer.dungan@duke.edu, Duke University School of Nursing.

Yvette P. Conley, Email: yconley+@pitt.edu, University of Pittsburgh School of Nursing, Department of Health Promotion & Development.

Taimour Y. Langaee, Email: langaee@cop.ufl.edu, University of Florida College of Pharmacy, Department of Pharmacy Practice.

Julie A. Johnson, Email: Johnson@cop.ufl.edu, University of Florida Colleges of Pharmacy and Medicine, Department of Pharmacy Practice and Cardiology.

Shawn M. Kneipp, Email: skneipp@nursing.ufl.edu, University of Florida College of Nursing, Health Care Environments and Systems Department.

Philip J. Hess, Email: hesspj@surgery.ufl.edu, University of Florida College of Medicine and Shands Hospital, Department of Thoracic and Cardiovascular Surgery.

Carolyn B. Yucha, Email: carolyn.yucha@unlv.edu, University of Nevada, Las Vegas, School of Nursing, Department of Physiological Nursing.

References

1. Anderson NB, McNeilly M, Myers H. Toward understanding difference in autonomic reactivity: A proposed contextual model. In: Turner JR, editor. Individual differences in cardiovascular response to stress. New York: Plenum Press; 1992. [Google Scholar]
2. Applied Biosystems. Technical bulletin #178: RNase and DEPC treatment: Fact or laboratory myth. 2008 Retrieved March 3, 2008, from www.ambion.com/techlib/tb/tb_178.html.
3. Bengtsson K, Orho-Melander M, Melander O, Lindblad U, Ranstam J, Ranstam L, et al. Beta(2)-adrenergic receptor gene variation and hypertension in subjects with type 2 diabetes. Hypertension. 2001;37:1303–1308. doi: 10.1161/01.hyp.37.5.1303. [DOI] [PubMed] [Google Scholar]
4. Brodde OE, Daul A, Michel MC. Subtype-selective modulation of human beta 1- and beta 2-adrenoceptor function by beta-adrenoceptor agonists and antagonists. Clinical Physiology and Biochemistry. 1990;8(Suppl 2):11–17. [PubMed] [Google Scholar]
5. Brodde OE, Daul AE, O’Hara N, Wang XL, Bock KD. Acute regulation of lymphocyte beta 2-adrenoceptors is altered in patients with essential hypertension. Journal of Hypertension. 1985;3(3):S129–S152. [PubMed] [Google Scholar]
6. Bustin SA. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): Trends and problems. Journal of Molecular Endocrinology. 2002;29:23–39. doi: 10.1677/jme.0.0290023. [DOI] [PubMed] [Google Scholar]
7. Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J, et al. Genetics of gene expression and its effect on disease. Nature. 2008;452:423–428. doi: 10.1038/nature06758. [DOI] [PubMed] [Google Scholar]
8. Feldman RD. β-adrenoceptor responsiveness in hypertension: Effects of dietary NaCl intake. British Journal of Pharmacology. 2000;30:55S–60S. doi: 10.1111/j.1365-2125.1990.tb05469.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Ferro A, Kaumann AJ, Brown MJ. Beta 1- and beta-2ADR-mediated relaxation in human internal mammary artery and saphenous vein: Unchanged beta- and alpha-ADR responsiveness after chronic beta 1-ADR blockade. British Journal of Pharmacology. 1993;109(4):1053–1058. doi: 10.1111/j.1476-5381.1993.tb13728.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Finnish Medical Society Duodecim. EBM guidelines. Helsinki, Finland: Duodecim Medical Publications Ltd; 2006. Coronary heart disease (CHD): Symptoms, diagnosis and treatment. Evidence-Based Medicine [CD-ROM] [Google Scholar]
11. Gaballa MA, Peppe K, Lefkowitz RJ, Aguirre M, Dober PC, Pennock, et al. Enhanced vasorelaxation by overexpression of beta-2-adrenergic receptors in large arteries. Journal of Molecular and Cellular Cardiology. 1998;30:1037–1045. doi: 10.1006/jmcc.1998.0668. [DOI] [PubMed] [Google Scholar]
12. Gros R, Benovic JL, Tan CM, Feldman RD. G-protein coupled receptor-kinase activity is increased in hypertension. Journal of Clinical Investigation. 1997;99:2087–2093. doi: 10.1172/JCI119381. [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Gros R, Chorazyczewski J, Meek M, Benovic JL, Ferguson SSG, Feldman RD. G-protein-coupled receptor kinase activity in hypertension increased vascular and lymphocyte GRK2 protein expression. Hypertension. 2000;35:38–42. doi: 10.1161/01.hyp.35.1.38. [DOI] [PubMed] [Google Scholar]
14. Gros R, Tan CM, Chorazyczewski J, Kelvin DJ, Benovic JL, Feldman RD. G-protein-coupled receptor kinase expression in hypertension. Clinical Pharmacology and Therapeutics. 1999;65:545–551. doi: 10.1016/S0009-9236(99)70074-3. [DOI] [PubMed] [Google Scholar]
15. Hantow IN, Koopmans RP, Michel MC. The beta2-adrenoceptor gene and hypertension: Is it the promoter or the coding region or neither? Journal of Hypertension. 2006;24:1003–1007. doi: 10.1097/01.hjh.0000226185.06063.80. [DOI] [PubMed] [Google Scholar]
16. Hindorff LA, Heckbert SR, Psaty BM, Lumley T, Siscovick DS, Herrington DM. Beta(2)-adrenergic receptor polymorphisms and determinants of cardiovascular risk: The Cardiovascular Health Study. American Journal of Hypertension. 2005;18:392–397. doi: 10.1016/j.amjhyper.2004.10.014. [DOI] [PubMed] [Google Scholar]
17. Iaccarino G, Cipolletta E, Fiorillo A, Annecchiarico M, Ciccarelli M, Cimini V, et al. β2-Adrenergic receptor gene delivery to the endothelium corrects impaired adrenergic vasorelaxation in hypertension. Circulation. 2002;106:349–355. doi: 10.1161/01.cir.0000022690.55143.56. [DOI] [PubMed] [Google Scholar]
18. Jennings K, Parsons V. A study of labetalol in patients of European, West Indian, and West African origin. British Journal of Clinical Pharmacology. 1976;3:773S–775S. [PubMed] [Google Scholar]
19. Joint National Committee on Prevention Detection and Evaluation and Treatment of High Blood Pressure. The seventh report of the Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure: The JNC 7 report. Journal of the American Medical Association. 2003;289:2560–2572. doi: 10.1001/jama.289.19.2560. [DOI] [PubMed] [Google Scholar]
20. Lacy CF, Armstrong LL, Golman MP, Lance LL. Drug Information Handbook. Hudson, Ohio: Lexi-Comp; 2005. [Google Scholar]
21. Michel MC, Galal O, Stoermer J, Bock KD, Brodde OE. Alpha- and beta-adrenoceptors in hypertension. II. Platelet alpha 2- and lymphocyte beta 2-adrenoceptors in children of parents with essential hypertension. A model for the pathogenesis of genetically determined hypertension. Journal of Cardiovascular Pharmacology. 1989;13(3):432–439. doi: 10.1097/00005344-198903000-00011. [DOI] [PubMed] [Google Scholar]
22. Middeke M, Remien J, Holzgreve H. Journal of Hypertension. 3. Vol. 2. 1984. The influence of sex, age, blood pressure and physical stress on beta 2 adrenoceptor density of mononuclear cells; pp. 261–264. [PubMed] [Google Scholar]
23. Monopoli A, Conti A, Forlani A, Ongini E. β1 and β2 adrenoceptors are involved in mediating vasodilation in the human coronary artery. Pharmacology Research. 1993;27:273–279. doi: 10.1006/phrs.1993.1026. [DOI] [PubMed] [Google Scholar]
24. Naslund T, Silberstein DJ, Merrell WJ, Nadeau JH, Wood AJJ. Low sodium intake corrects abnormality in B-receptor mediated arterial vasodilation in patients with hypertension: Correlation with B-receptor function in vitro. Clinical Pharmacology and Therapeutics. 1990;48:87–95. doi: 10.1038/clpt.1990.121. [DOI] [PubMed] [Google Scholar]
25. Peuster M, Fink C, Reckers J, Beerbaum P, von Schnakenburg C. Assessment of subacute inflammatory and proliferative response to coronary stenting in a porcine model by local gene expression studies and histomorphometry. Biomaterials. 2004;25:957–963. doi: 10.1016/s0142-9612(03)00613-6. [DOI] [PubMed] [Google Scholar]
26. Pippig S, Adnexinger S, Daniel K, Puzicha M, Caron MG, Lefkowitz RJ, et al. Overexpression of beta-arrestin and beta-adrenerger receptor kinase augment desensitization of beta 2-adrenergic receptors. Journal of Biological Chemistry. 1993;268:3201–3208. [PubMed] [Google Scholar]
27. Pitcher J, Lohse MJ, Codina J, Caron MG, Lefkowitz RJ. Desensitization of the isolated beta 2-adrenergic receptor by beta-adrenergic receptor kinase, cAMP-dependent protein kinase, and protein kinase C occurs via distinct molecular mechanisms. Biochemistry. 1992;31:3193–3197. doi: 10.1021/bi00127a021. [DOI] [PubMed] [Google Scholar]
28. Small KM, McGraw DW, Liggett SB. Pharmacology and physiology of human adrenoceptor polymorphisms. Annual Review of Pharmacology and Toxicology. 2003;43:381–411. doi: 10.1146/annurev.pharmtox.43.100901.135823. [DOI] [PubMed] [Google Scholar]
29. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology. 2002;3(7):research0034.1–0031.11. doi: 10.1186/gb-2002-3-7-research0034. [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Veglio F, Tayebati SK, Schiavone D, Ricci A, Mulatero P, Bronzetti E, et al. Alpha-1-adrenergic receptor subtypes in peripheral blood lymphocytes of essential hypertensives. Journal of Hypertension. 2001;19:1847–1854. doi: 10.1097/00004872-200110000-00020. [DOI] [PubMed] [Google Scholar]
31. Wang T, Brown MJ. Influence of β1-adrenoceptor blockade on the gene expression of adenylate cyclase subtypes and β-adrenoceptor kinase in human atrium. Clinical Science. 2001;101:211–214. [PubMed] [Google Scholar]
32. Wood AJ. Variability in β-adrenergic receptor response in the vasculature: Role of receptor polymorphism. Journal of Allergy and Clinical Immunology. 2002;110:S318–321. doi: 10.1067/mai.2002.129701. [DOI] [PubMed] [Google Scholar]