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Hypertension. 1995;25:320-326

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(Hypertension. 1995;25:320-326.)
© 1995 American Heart Association, Inc.

Articles

Association of the {alpha}-Adducin Locus With Essential Hypertension

Giorgio Casari; Cristina Barlassina; Daniele Cusi; Laura Zagato; Roslyn Muirhead; Marco Righetti; Paola Nembri; Karen Amar; Massimo Gatti; Fabio Macciardi; Giorgio Binelli; Giuseppe Bianchi

From the Division of Nephrology, Dialysis and Hypertension, University of Milan, S. Raffaele Hospital; Prassis-Sigma-Tau Research Institute, Settimo Milanese, Milan; the Blood Transfusion and Immunology of Transplantation Centre, Ospedale Maggiore Policlinico, Milan; the Department of Neuroscience, University of Milan, S. Raffaele Hospital; and the Department of Genetics and Microbiology, University of Milan (Italy).

Correspondence to Prof Giuseppe Bianchi, University of Milan, Division of Nephrology, Dialysis and Hypertension, S. Raffaele Hospital, via Olgettina 60, 20132 Milan, Italy.


*    Abstract
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 *Abstract
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Abstract Previous studies on genetic rat hypertension have shown that polymorphism within the {alpha}-adducin gene may regulate blood pressure. Adducin is a cytoskeletal protein that may be involved in cellular signal transduction and interacts with other membrane-skeleton proteins that affect ion transport across the cell membrane. There is a high homology between rat and human adducin and pathophysiological similarities between the Milan hypertensive rat strain and a subgroup of patients with essential hypertension. Thus, we designed a case-control study to test the possible association between the {alpha}-adducin locus and hypertension. One hundred ninety primary hypertensive patients were compared with 126 control subjects. All subjects were white and unrelated. Four multiallelic markers surrounding the {alpha}-adducin locus located in 4p16.3 were selected: D4S125 and D4S95 mapping at 680 and 20 kb centromeric, and D4S43 and D4S228/E24 mapping at 660 and 2500 kb telomeric. Alleles for each marker were pooled into groups. Comparisons between control subjects and hypertensive patients were carried out by testing the allele-disease association relative to the marker genotype. The maximal association occurred for D4S95 ({chi}12 13.33), which maps closest to {alpha}-adducin. These data suggest that a polymorphism within the {alpha}-adducin gene may affect blood pressure in humans.


Key Words: case-control studies • hypertension, essential • polymorphism (genetics) • cytoskeleton • genes


*    Introduction
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 up arrowAbstract
 *Introduction
down arrowMethods
 down arrowResults
 down arrowDiscussion
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Essential or primary hypertension is a heterogeneous risk factor in terms of organ complications, response to therapy, and causes, which include both polygenic and environmental components. At least 30% of the blood pressure (BP) variability is attributable to genetic variation among individuals.1 As we have discussed elsewhere,2 it is likely that the different animal models of genetic hypertension may represent a portion of the clinical heterogeneity. The Milan hypertensive strain of rats (MHS) shows many pathophysiological analogies with a subgroup of hypertensive humans.2 3 Up to a few years ago, only circumstantial evidence supported these analogies based mainly on measurements of renal function and membrane ion transport, which is faster in both rats and a subgroup of patients.3 4 5 However, recently a series of findings obtained at intracellular and molecular levels by comparing the MHS and Milan normotensive strain (MNS) suggested that the membrane skeleton protein adducin could be involved in causing the faster ion transport across the cell membrane of MHS.4 6 7 8 9 10 Therefore, the comparison between rats and humans can be carried out using this molecular probe.

Adducin migrates from the cytoplasm to the cell-to-cell contact sites in a calcium- and phosphorylation-dependent manner in renal tubular cells in culture.11 Adducin favors the spectrin-actin interaction, thus affecting the structure of the cell membrane skeleton. The precise role of adducin is unknown; however, in view of the proposed role of actin, ankirin, and other skeleton membrane proteins in the regulation of cell membrane ion transport,12 13 we postulated that adducin polymorphism may also be involved in the regulation of BP.

Adducin is a dimer with {alpha}- and ß-subunits whose genes map in both humans and rats on different chromosomes14 (G.C., unpublished observation). The determination of the MHS and MNS adducin cDNA sequences showed one point mutation in each of the two genes coding for the {alpha}- and ß-subunits of adducin.10 The genetic analysis in rats showed that the mutation of {alpha}-adducin alone could account for the most significant BP variation, whereas the mutation on the ß-subunit was only modulating the effect of the {alpha}-subunit.10

On the assumption that hypertension in humans may also be influenced by functional mutations within the {alpha}-adducin gene, which in turn is in linkage disequilibrium with DNA markers mapping close to the adducin locus, we designed a case-control study for testing a possible association between 4p16.3 marker polymorphism and hypertension. The results show a highly significant association of the minisatellite D4S95 with hypertension. The association weakens and disappears for markers mapping farther from the adducin locus. Therefore, our results are consistent with the hypothesis that {alpha}-adducin polymorphism may account for a significant portion of BP genetic variation in humans.


*    Methods
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 up arrowIntroduction
 *Methods
down arrowResults
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Patients and Control Subjects
One hundred ninety essential hypertensive patients and 126 normotensive control subjects were recruited for the study. All of them were white and unrelated; they were living within the metropolitan area of Milan and were free of any other disease. All members of restricted communities (eg, Jews, Armenians, North Africans) were excluded a priori from cases and control subjects. Hypertensive patients were recruited from two different sources: the Hypertension Outpatient Clinic of the Division of Nephrology of the San Raffaele Hospital of Milan and the Blood Transfusion and Transplantation Immunology Centre, Ospedale Maggiore, Milan. Control subjects were inpatients recruited from the Departments of orthopedic surgery, urology, and ophthalmology of the San Raffaele Hospital, Milan, where they had been admitted for minor surgery.

To avoid recruitment of false-positive or secondary hypertensive patients and false-negative control subjects, we applied the following inclusion criteria. All hypertensive patients underwent standard screening for secondary forms of hypertension. Selected hypertensive patients either had a BP higher than 150/95 mm Hg, if recruited before the onset of antihypertensive treatment, or were under antihypertensive treatment at the time of blood sampling. Moreover, each patient was less than 60 years of age before the onset of hypertension. BP information and data on the initiation of pharmacological treatment were available for each subject. For those patients considered as urgently needing pharmacological treatment, only one pretreatment value was available. The less severe hypertensive patients (n=160) were routinely monitored for up to 3 months before the start of pharmacological treatment (so that three measurements of BP in three consecutive visits at the referral center were available). The mean duration of hypertension in our sample was 8.02±0.5 years.

To exclude false-negative control subjects with late onset of hypertension, subjects were older than 60 years, had not been diagnosed or treated as hypertensive earlier in life, had a negative (self-reported) family history of hypertension, and had a BP value less than 150/85 mm Hg. They were in all cases recruited before surgery, and their BP values were recorded several times during their stay in the hospital.

Informed consent to this study was obtained from each individual recruited.

Genotype Analysis and Clustering of Alleles
Genomic DNA was isolated from 3 mL of whole blood following a standard procedure.15 The four markers surrounding the {alpha}-adducin gene—D4S12516 and D4S9517 centromeric, and D4S4318 and D4S228/E2419 telomeric, mapping 680, 20, 660, and 2500 kb from the {alpha}-adducin locus, respectively—were selected for their high polymorphism information content values and the possibility of rapid analysis by polymerase chain reaction (PCR). To increase sensitivity and interexperiment reproducibility, we used nondenaturing polyacrylamide (5% or 6%) gel electrophoresis to separate D4S125 and D4S95 allelic forms, thus increasing the number of resolvable alleles to 28 and 30, respectively. Allelic bands were fixed and silver stained.20 The 28 different alleles of D4S125 range from 1600 to 2400 bp as reported.16 The 30 allelic forms of D4S95 span from 990 to 1600 bp as in Allitto et al.17 After PCR cycling, D4S43 and D4S228/E24 were resolved with a denaturing 6% or 5% polyacrylamide gel, respectively. Gels were blotted onto positively charged nylon membranes and hybridized with 32P-labeled D4S43 or D4S228/E24 PCR product. Sixteen alleles, from 184 to 478 bp, were detected with marker D4S43. Microsatellite D4S228/E24 gave nine different alleles, differing from each other by a single CA repeat. For convenience, all alleles of each marker were numbered from the shortest (allele 1) to the longest (allele n). To obtain an accurate and reproducible reading of the genotypes from gel to gel, we loaded onto each gel a specific minisatellite marker generated by pooling several single PCR products from DNAs of known genotype and two commercial molecular weight DNA markers (FX HaeIII digested and 1-kb ladder from BRL). All genotype determination underwent double-blind scoring to minimize gel reading errors.

To overcome the problem of dealing with the high number of different allelic forms resolved for each marker, we clustered alleles, thus reducing the number of comparisons and the risk of type 1 errors. Clustering is implied by the fact that the different number of tandem repeats resulting in the corresponding alleles is not evenly scattered (between the shortest and longest); that is, they tend to concentrate in groups separated by a "valley" of a few or, as for D4S43 and D4S95, of missing allelic forms. Mutations generating alleles of new lengths occur much more frequently at hypervariable loci than in coding regions and are probably due to deletion or duplication of one or more repeat units arising by sister chromatid exchange or DNA slippage during replication.21 22 In fact, the gain or loss of one or a few minisatellite repeats is much more frequent than the appearance of new allelic forms deriving from the insertion or deletion of a large number of minisatellite repeat units.23 This supports the hypothesis that every cluster of alleles originated from a common ancestor, which displayed a number of repeat units close (or corresponding) to the average number of repeats of the considered cluster. Thus, a progenitor of an allele cluster is assumed to be linked to a cosegregating mutation in a gene close to the marker, in our case {alpha}-adducin.

The clustering method is based on the pattern of allelic frequency distribution shown by the examination of the normal probability plot and the detrended normal probability plot and later confirmed by study of the frequency distribution of the raw data. We used this descriptive statistical method, which approximates to normal the commingling distributions. In the normal probability plot, each observed value was paired with its expected value from the normal distribution. The expected value from the normal distribution is based on the number of cases and the rank order of the case in the sample. If the sample is from a normal distribution, we expect that the points will fall on a straight line. Visually, it is more practical to look at the detrended plot, which expresses the difference between observed and expected values (Fig 1, bottom left). Each change of slope on the normal probability plot or each inversion of direction for the detrended plot corresponds to a minimum (a valley) for the distribution, thereby representing the possible boundaries of natural allele groups. Fig 1 summarizes the data for D4S95, the closest marker to {alpha}-adducin. Alternatively, clusters of alleles were also grouped "binomially" into short versus long ones, similar to what was previously done for the apolipoprotein B 3' variable number tandem repeat (VNTR).24 Although such binomial division is evidently an arbitrary one, it has the practical consequence of reducing to two the possible number of allelic forms for each marker, making the analysis simpler and allowing the analysis for genotype.



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  		Figure 1. Graphs show frequency distribution of D4S95 alleles. Right, relative frequency of chromosomes for each observed allele are plotted. Left, hypertensive patients and normotensive subjects were pooled. The top panel presents data plotted with a normal probability plot; the bottom panel, data plotted with a detrended normal probability plot. Arrows indicate the correspondence of the three graphic methods in identifying the boundaries between the clusters.

Statistical Methods
All clinical parameters are expressed as mean±SEM. Mean BP values and anthropometric variables were compared with Student's t test. Sex ratios and allele-disease association were tested by {chi}2. Statistical analysis was performed with the use of SPSS (version 4) statistical software.


*    Results
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 up arrowMethods
 *Results
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The main clinical characteristics of hypertensive patients and control subjects are summarized in Table 1; the average systolic and diastolic BP values of hypertensive patients is the one obtained before antihypertensive treatment was started. Because of the study design, the control subjects were older than hypertensive patients; therefore, no statistical comparison for age is provided as it is for BP values. The male/female ratio was significantly different between hypertensive patients and control subjects. Height, weight, and body mass index (BMI) of hypertensive patients were greater than those of control subjects.


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  		Table 1. Main Characteristics of Study Groups

The frequency distribution obtained with D4S43 on 81 unrelated whites previously studied18 was compared with our normotensive and hypertensive groups: the first was quite similar, and the latter showed different frequencies, in particular for allele 1 (see Reference 1818 : 25%; normotensive, 26.2%; hypertensive, 36.3%; P<.01) and allele 11 (see Reference 18: 16%; normotensive, 15.1%; hypertensive, 9.5%; P<.05). No frequency distribution data were available for D4S228/E24. Previous articles involving linkage analysis with markers D4S125 and D4S95 by differential allelic migration on agarose gel electrophoresis detected 925 and 1526 different alleles, respectively. Therefore, no comparison can be made with our results because a larger number of alleles was detected in our experimental conditions.

Genotyping and Clustering of Alleles
From the data provided by the analysis of the distribution for each marker, we extrapolated the following major groups of alleles (the cutoff points are indicated by the arrows in Fig 1) for marker D4S95: First group <=13; second group >13 but <=22; and third group >22. An identical approach was followed for clustering the alleles of other markers. D4S125 was divided into four major clusters of alleles (<=7, >7 but <=14, >14 but <=20, and >20), D4S43 into three major clusters (<=3, >3 but <=8, and >8), and D4S228/E24 was again divided into three major clusters (<=4, >4 but <=5, and >5).

Allele-Disease Association
Table 2 summarizes the data of the association study for the four markers with hypertension. D4S95 and D4S43 allelic distribution was significantly different between control subjects and hypertensive patients. The binomial division cutoff points into short versus long alleles were alleles 13 and 14 for D4S95, 14 and 15 for D4S125, 3 and 4 for D4S43, and 5 and 6 for D4S228/E24. The results of the allele-disease association using such binomial division cutoff points are summarized in Fig 2. They are in agreement with results obtained by considering all major clusters and show a significant association between markers D4S125, D4S95, and D4S43 and hypertension.


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  		Table 2. {chi}2 Values of Allelic Frequency Distribution Analysis



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  		Figure 2. Bar graph shows {chi}2 values for the analysis of allelic distribution between hypertensive patients and normotensive subjects. {chi}2 values can be directly compared because they have the same degrees of freedom.1 Horizontal line indicates the critical significance level. The chromosomal positions of the studied markers are indicated, together with the genes already described in the same region of chromosome 4: 1, Huntington's disease–related gene; 2, G protein–coupled receptor kinase; 3, {alpha}-adducin; 4, fibroblast growth factor receptor 3; 5, {alpha}-L-iduronidase; and 6, cyclic GMP phosphodiesterase ß.

Interference of Other Clinical Variables
Because of the study design, our control subjects were older than the hypertensive patients. Also, differences in BMI and sex distribution between the groups were present. Since any of these differences may have contributed per se to determining the attainment of the allele-disease association, we performed the following tests to exclude this possibility.

Effect of Age
The effect of age was tested in three ways. First, 29 hypertensive patients were older than 60 (mean age, 65.8±1.02 years) at the time of blood sampling, since their hypertension was diagnosed many years before. No difference of allelic distribution for each marker was found when these subjects were compared with the hypertensive patients younger than 60 (mean age, 48.1±0.62 years); however, the number of old hypertensive patients is small. Second, the analysis was repeated using as a cutoff the median of age (51 years for the hypertensive patients, 69 for the normotensive subjects), so that two groups of approximately the same size were found. No difference of allelic distribution for each marker was found by comparing hypertensive patients (or normotensive subjects) younger than the median for their age versus hypertensive patients (or normotensive subjects) older than the median for their age (data not shown). Third, we compared the distribution of each marker only in hypertensive patients older than 51 (the median value for the age of all hypertensive patients) and normotensive subjects younger than 76 (which corresponds to the oldest age of the hypertensive patients). Despite having halved the difference in age between hypertensive patients and control subjects (58.4±0.35 and 67.4±0.35, respectively), results very similar to those for the total sample were obtained ({chi}2 and significance levels for D4S125, D4S95, D4S43, and D4S228/E24, respectively: {chi}12 2.31, P=.13; {chi}12 7.0, P=.008; {chi}12 7.2, P=.007; and {chi}12 0.01, P=.90).

Effect of BMI
The effect of BMI was tested by removing individuals with extreme BMI values (<20 and >28 kg/m2). When the analysis was limited to 102 control subjects and 137 hypertensive patients with mean BMI values of 24.0±0.21 and 24.7±0.16 kg/m2, respectively, the results were similar to those obtained for the total sample ({chi}2 and significance levels for D4S125, D4S95, D4S43, and D4S228/E24, respectively: {chi}12 1.95, P=.16; {chi}12 11.75, P=.0006; {chi}12 7.64, P=.006; {chi}12 0.10, P=.75). As for age, the analysis was also repeated using as a cutoff the median of BMI (for hypertensive patients, 25.88 kg/m2; for normotensive subjects, 23.82 kg/m2) so that two groups of the same size were found. No difference of allelic distribution for each marker was found by comparing hypertensive patients (or normotensive subjects) whose BMI was smaller than the median of their BMI with hypertensive patients (or normotensive subjects) whose BMI was greater than the median of their BMI (data not shown).

Effect of Sex
No significant difference in allelic distribution between men and women was seen when our sample was considered globally ({chi}2 and significance levels for D4S125, D4S95, D4S43, and D4S228/E24, respectively: {chi}12 0.056, P=.81; {chi}12 2.86, P=.91; {chi}12 0.113, P=.74; {chi}12 0.773, P=.38) or after hypertensive patients alone or normotensive subjects alone were considered (data not shown).


*    Discussion
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 *Discussion
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This study shows a significant difference between hypertensive patients and control subjects in the allelic distribution of the markers mapping close to the {alpha}-adducin gene. Moreover, the most significant difference occurs with a DNA marker on the short arm of chromosome 4 (4p16.3), very close (approximately 20 kb) to the {alpha}-adducin locus, whereas the marker mapping far from the {alpha}-adducin locus (approximately 2500 kb) did not reach statistical significance. These findings are consistent with the working hypothesis originating from the studies on the rat model and point to a very narrow DNA region close to (or corresponding to) the {alpha}-adducin locus. However, a gene-disease association arising from a case-control study should be considered with caution because of possible sampling errors.27

We have previously shown that hypertension tends to be a recessive trait in rats.28 It is likely that such a mode of inheritance may also occur in humans if the same genetic mechanism is involved. For this reason, young normotensive offspring of normotensive parents could develop hypertension later in life because of homozygosity of recessive alleles. Therefore, we were compelled to use as control subjects only individuals who were still normotensive after the age of 60 years. By selecting the control subjects older than 60, we minimized the number of hypertensive alleles in this population. However, because of the age difference between hypertensive patients and control subjects, it is not possible to exclude a selective accumulation of alleles independent of hypertension as age progresses. Therefore, we analyzed the effect of age as well as the influence of BMI and gender in the present study. The distribution of the alleles of each marker studied was not affected by age. Despite 14.8 years of age difference between hypertensive patients younger and older than the median age of 51 years, no difference in allelic distribution for each marker was found. Similar results were obtained for the comparison of hypertensive patients younger and older than 60. On the contrary, when only the hypertensive patients older than the median are considered, the significant difference for allelic distribution between control subjects and hypertensive patients remains.

The removal from both groups of a subset of individuals who were at the extreme of BMI distribution did not affect the significance of the allele-disease association, ruling out the possible influence of this variable. Finally, no sex effect could be detected.

The different allelic distribution between hypertensive patients and control subjects seems to focus on the narrow region containing the {alpha}-adducin locus. Most importantly, the allelic distribution of the normotensive population reported here for the D4S43 marker is similar to the distribution reported in a different white population.18 On the contrary, the same comparison with our hypertensive patients showed different frequencies for alleles 1 and 11.

To date, many candidate genes coding for proteins known to be involved in BP regulation have been considered for genetic studies in both animal models of hypertension and humans with primary hypertension. The results obtained in these studies have suggested a genetic role for angiotensinogen,29 aldosterone synthase,30 and, controversially, angiotensin-converting enzyme31 32 in humans as well as renin,33 34 11ß-hydroxylase,35 kallikrein,36 and angiotensin-converting enzyme37 38 in rats.

Our approach was different. We addressed our attention to adducin as a candidate for genetic studies in MHS after a long series of observations on renal transplantation39 and function40 41 and ion transport across the cell membrane of intact cells or membrane vesicles either with42 43 44 45 46 or without47 membrane skeleton. These observations were consistent with the hypothesis that hypertension in MHS was caused by a faster ion transport across cell membrane, which in turn was sustained by an abnormal membrane skeleton protein, subsequently identified as adducin.8

Because of these findings in rats, the high degree of amino acid homology between rat and human adducin (94%, unpublished observation), and pathophysiological similarities between MHS and a subgroup of patients with primary hypertension,2 we tested the hypothesis that the {alpha}-adducin locus could be involved in BP regulation in humans. The results described here are consistent with this hypothesis.

Our data do not exclude the possibility that other genes mapping in the same region (both known—such as the one coding for fibroblast growth factor receptor 3,48 {alpha}-L-iduronidase,49 ß-polypeptide of cyclic GMP phosphodiesterase [PDEB],50 G protein–coupled receptor kinase,51 and the Huntington's disease–related protein52 —or unknown) could be in linkage disequilibrium with the D4S95 marker and also contribute to the genetic BP variation.

The adducin polymorphism might account for only a portion of genetic variation of BP, in agreement with the common and well-supported hypothesis that hypertension is a heterogeneous and polygenic disease. In MHS the ß-adducin gene polymorphism modulates the influence of {alpha}-adducin mutation on BP, but certainly many other epistatic interactions are possible in both rats and humans.

In conclusion, our findings relating to both rats and humans are consistent with the hypothesis that a constitutive cell membrane skeleton protein may contribute to BP control in two mammalian species that diverged approximately 50 to 60 million years ago.53


*    Acknowledgments
 
This work was supported in part by Ministero Università e Ricerca Scientifica of Italy (MPI 60%, years 1990-1993 to C.B., D.C., and G.B., and MPI 40%, years 1990-1993 to D.C.) and by Consiglio Nazionale delle Ricerche grant No. 93.00343.PF40.115.25586; also supported in part by Eurhypgen grant. The authors are grateful to Dr Giovanna Cremonesi and Prof Girolamo Sirchia (Blood Transfusion and Immunology of Transplantation Centre, Ospedale Maggiore Policlinico, Milano) and Dr Paola Stella (Division of Nephrology, Dialysis and Hypertension, S. Raffaele Hospital, Milano) for their help in selecting part of the cases and control subjects.

Received March 29, 1994; first decision May 4, 1994; accepted October 25, 1994.


*    References
up arrowTop
 up arrowAbstract
 up arrowIntroduction
 up arrowMethods
 up arrowResults
 up arrowDiscussion
 *References
 

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