Skip to main content
Download PDF

Association of APP gene polymorphisms and promoter methylation with essential hypertension in Guizhou: a case–control study

Human Genomics volume 17, Article number: 25 (2023) Cite this article

Abstract

Background

Single-nucleotide polymorphisms (SNPs) and DNA methylation are crucial regulators of essential hypertension (EH). Amyloid precursor protein (APP) mutations are implicated in hypertension development. Nonetheless, studies on the association of APP gene polymorphism and promoter methylation with hypertension are limited. Therefore, this case–control aims to evaluate the genetic association of APP gene polymorphism and promoter methylation with EH in Guizhou populations.

Objective and methods

We conducted a case–control study on 343 EH patients and 335 healthy controls (including Miao, Buyi, and Han populations) in the Guizhou province of China to analyze 11 single-nucleotide polymorphisms (rs2040273, rs63750921, rs2211772, rs2830077, rs467021, rs368196, rs466433, rs364048, rs364051, rs438031, rs463946) in the APP gene via MassARRAY SNP. The MassARRAY EpiTYPER was employed to detect the methylation levels of the promoters.

Results

In the Han population, the rs2211772 genotype distribution was significantly different between disease and control groups (χ2 = 6.343, P = 0.039). The CC genotype reduced the risk of hypertension compared to the TT or TC genotype (OR 0.105, 95%CI 0.012–0.914, P = 0.041). For rs2040273 in the Miao population, AG or GG genotype reduced the hypertension risk compared with the AA genotype (OR 0.533, 95%CI 0.294–0.965, P = 0.038). Haplotype TCC (rs364051–rs438031–rs463946) increased the risk of EH in Guizhou (OR 1.427, 95%CI 1.020–1.996, P = 0.037). Each 1% increase in CpG_19 (− 613 bp) methylation level was associated with a 4.1% increase in hypertension risk (OR 1.041, 95%CI 1.002–1.081, P = 0.039). Each 1% increase in CpG_1 (− 296 bp) methylation level was associated with an 8% decrease in hypertension risk in women (OR 0.920, 95%CI 0.860–0.984, P = 0.015). CpG_19 significantly correlated with systolic blood pressure (r = 0.2, P = 0.03). The methylation levels of CpG_19 in hypertensive patients with rs466433, rs364048, and rs364051 minor alleles were lower than that with wild-type alleles (P < 0.05). Moreover, rs467021 and rs364051 showed strong synergistic interaction with EH (χ2 = 7.633, P = 0.006). CpG_11, CpG_19, and rs364051 showed weak synergistic interaction with EH (χ2 = 19.874, P < 0.001).

Conclusion

In summary, rs2211772 polymorphism and promoter methylation level of APP gene may be linked to EH in Guizhou populations. Our findings will provide novel insights for genetic research of hypertension and Alzheimer's disease.

Introduction

In a national survey of hypertension in 2018, the prevalence of hypertension among Chinese adults was 24.7% (approximately 274 million). Hypertension is a serious health problem in China [1]. Notably, essential hypertension (EH) is a complex multifactorial disease. The effect of genetic factors on blood pressure changes varies from 30 to 50% and regulates blood pressure [2]. A single-nucleotide polymorphism (SNP) is a DNA sequence polymorphism caused by variation of a single nucleotide at a specific site in the genomic DNA sequence. In recent years, several SNPs associated with blood pressure have been reported [3]. DNA methylation also regulates blood pressure genes, potentially disrupting the phenotype and function of the vessel wall in response to environmental stress [4, 5]. Heritable epigenetic changes may partly explain the genetic absence of hypertension [6]. A genome-wide association study identified 12 novel loci of genetic variations associated with blood pressure. These sites showed differential DNA methylation in hypertension patients. DNA methylation may mediate the association of SNP variants with blood pressure [7].

The amyloid precursor protein (APP) is a gene located in the 21q21.3 region of human chromosomes and encodes the production of the amyloid precursor protein. β-Amyloid (Aβ) is a polypeptide produced via hydrolysis of amyloid precursor proteins mediated by β- and γ-secretases; it circulates in the blood, cerebrospinal fluid, and interstitial fluid [8]. APP gene mutations increase Aβ production and aggregation [9]. Aβ accumulation causes abnormal cerebral vascular metabolism, increased angiotensin II, and sympathetic nerve activity, resulting in hypertension [10]. The APP gene promoter region has a high frequency of CpG dinucleotides, increasing the possibility that DNA cytosine methylation participates in the regulation of APP expression [11]. Studies have shown that the CpG site on the APP gene promoter is hypomethylated in Alzheimer's disease (AD) of the brain, causing the overexpression of amyloid precursor protein, hence overproduction and abnormal metabolism of Aβ [12]. Significant methylation differences occur at 11 CpG sites in the APP gene between patients with traumatic brain injury and idiopathic normal pressure hydrocephalus [13]. Lead (Pb) potentially exerts neurotoxic effects by changing the overall methylation and promoter methylation patterns of the APP gene [14].

Nonetheless, reports on the association of APP gene polymorphism and promoter methylation with hypertension are limited. Therefore, we used the MassARRAY SNP (Agena Bioscience, Inc.) to detect 11 single-nucleotide polymorphisms (rs2040273, rs63750921, rs2211772, rs2830077, rs467021, rs368196, rs466433, rs364048, rs364051, rs438031, rs463946) in the APP gene under a case–control method. Notably, MassARRAY EpiTYPER detects methylation levels of promoters (GRCh38/hg38; chr21:26,171,035–26,171,512). This study aims to explore the genetic association of APP gene polymorphism and promoter methylation with EH in the Guizhou population and provide a theoretical basis for genetic pathogenesis, prevention as well as control approaches to EH.

Results

Basic demographic characteristics of the population

In different populations of Guizhou, we found no significant difference in gender and BMI between the hypertension group and the control group (P > 0.05). The average age of the hypertension group was greater than that of the control group, and this difference was statistically significant (P < 0.05) (Tables 1, 2).

Table 1 Basic characteristics of SNP research objects
Full size table
Table 2 Basic characteristics of methylation research subjects
Full size table

Risk factors for hypertension

A binary multivariate logistic regression model was constructed considering hypertension as the dependent variable and age, gender, as well as BMI as independent variables. For the general population of Guizhou, age, overweight, and obesity were independent risk factors for EH (OR 1.044, 95%CI 1.031–1.057, P = 0.001; OR 1.611, 95%CI 1.127–2.302, P = 0.009 and OR 2.065, 95%CI 1.248–3.418, P = 0.005) (Table 3).

Table 3 Risk factors of essential hypertension in Guizhou populations (n = 678)
Full size table

Allele and genotype distribution

No mutant bases were detected at the rs63750921 site. The distributions of other SNPs were in accordance with the Hardy–Weinberg equilibrium (P > 0.05). We compared the allele and genotype frequencies of different SNPs of APP in different populations. The results revealed that the distribution of rs2211772 genotype between the disease group and the control group was significantly different in the Han population (χ2 = 6.343, P = 0.039). No significant difference was observed in the Miao and Buyi populations (P > 0.05). The distribution difference of rs2040273 allele and genotype between the disease group and control group was at a critical value in the Miao population (χ2 = 3.795, P = 0.051 and χ2 = 4.711, P = 0.095) (Additional file 1: Table S1).

Genetic pattern analysis

Further, binary logistic regression analysis was used to analyze the inheritance pattern of rs2211772 associated with hypertension in the Han population and rs2040273 in the Miao population. After adjusting for age, gender, and BMI confounding factors, the CC genotype reduced the risk of hypertension for the rs2211772 in the Han population, compared with the TT or TC genotype (OR 0.105, 95%CI 0.012 -0.914, P = 0.041). AG or GG genotype reduced the hypertension risk for the rs2040273 in the Miao population, compared with the AA genotype (OR 0.533, 95%CI 0.294–0.965, P = 0.038) (Table 4).

Table 4 Genetic patterns analysis of APP in Guizhou Han population
Full size table

Linkage disequilibrium and haplotype analysis

Linkage disequilibrium analysis was performed on all SNPs using the Haploview 4.2 software (except for the rs63750921 where no mutation was detected). Figure 1 shows the results of linkage disequilibrium analysis for the general population of Guizhou. D′ = 0 indicated complete linkage equilibrium; D′ = 1 indicated complete linkage disequilibrium, and D′ > 0.8 implied strong linkage disequilibrium, which was represented by the red square. As shown, rs364051–rs438031–rs463946 had strong a linkage disequilibrium; rs2040273–rs2211772 had weak linkage disequilibrium. Haplotype analysis was performed using the SHEsis online software. Consequently, the haplotype TCC constructed with rs364051–rs438031–rs463946 increased the risk of EH in the Guizhou population (OR 1.427, 95%CI 1.020–1.996, P = 0.037); the one constructed with rs2040273–rs2211772 haplotype AC increased the risk of EH in Guizhou Miao population (OR 2.330, 95%CI 1.284–4.226, P = 0.004); GC increased the risk of EH in Guizhou Buyi population (OR 1.997, 95%CI 1.008–3.956, P = 0.044), (Table 5).

Fig. 1
figure 1

APP gene linkage disequilibrium analysis (D′). D′ = 0 meant complete linkage equilibrium, D′ = 1 meant complete linkage disequilibrium, and  > 0.8 meant strong linkage disequilibrium

Full size image
Table 5 Haplotype association analysis between EH group and control group in Guizhou population
Full size table

The methylation level of APP gene promoter

The distribution of methylation results was skewed, hence expressed as medians (quartiles). The comparisons between groups were performed using the Mann–Whitney test. As a consequence, the methylation level of CpG_10 in the hypertension group was lower than that in the control group, whereas the methylation level of CpG_19 was higher than that in the control group (Fig. 2A). We found significant differences in the methylation levels of CpG_10 and CpG_19 between the hypertension group and the control group (z = − 2.024, P = 0.043 and z = − 2.721, P = 0.007) (Table 6). CpG_1 and CpG_11 were significantly different in the female hypertension group and control group (P = 0.018 and P = 0.009). The methylation levels in the hypertension group were lower than that in the control group (Fig. 2C/D). The heat map of cluster analysis revealed that CpG_11, CpG_19, and CpG_20 formed a cluster branch, all of which had high methylation levels (Fig. 2B).

Fig. 2
figure 2

Analysis of methylation results. A Boxplot of methylation levels at CpG sites in the APP gene promoter (n = 119). B Heatmap of cluster analysis of methylation results (n = 119). C Differences in CpG_1 methylation level between women with hypertension and control groups (n = 59). D Differences in CpG_11 methylation level between women with hypertension and control groups (n = 59). *P < 0.05. **P < 0.01

Full size image
Table 6 Methylation levels of CpG sites in APP gene promoter region
Full size table

Association of APP gene promoter methylation with hypertension

Binary logistic regression analysis was performed on the methylation results. After adjusting for age, the results showed that for every 1% increase in CpG_10 methylation level, the risk of hypertension decreased by 32.4% (OR 0.676, 95%CI 0.467–0.977, P = 0.037). Every 1% increase in CpG_19 methylation level was associated with a 4.1% higher risk of hypertension (OR 1.041, 95%CI 1.002–1.081, P = 0.039). With every 1% increase in CpG_1 methylation level, the risk of hypertension in women was reduced by 8% (OR 0.920, 95%CI 0.860–0.984, P = 0.015) (Table 7). The correlation analysis between the methylation level of CpG sites and blood pressure showed a positive correlation between CpG_19 and systolic blood pressure (r = 0.2, P = 0.03) (Fig. 3).

Table 7 Binary logistic regression analysis of methylation results
Full size table
Fig. 3
figure 3

Correlation between APP gene CpG_19 and systolic blood pressure (n = 119)

Full size image

Association of APP gene SNPs with methylation

The APP genes rs466433, rs364048, and rs364051 were located in the promoter region. We compared the differences in methylation levels of APP gene CpG_10 and CpG_19 in hypertensive patients with different genotypes. The methylation levels of CpG_19 in hypertensive patients carrying minor alleles of rs466433, rs364048, and rs364051 were lower than that of patients carrying wild-type alleles; the difference was statistically significant (Fig. 4).

Fig. 4
figure 4

The relationship between APP gene promoter SNP genotype and methylation level in hypertensive patients (n = 60). *P < 0.05. **P < 0.01

Full size image

Interaction analysis of APP gene SNP and promoter methylation

Using the MDR3.0.2 software, we analyzed the interaction effects of SNP-SNP, CpG-CpG, and SNP-CpG, and established the optimal interaction model including 1–3 sites. For the general population of Guizhou, the best combination of two SNPs interaction was rs467021–rs364051, the cross-validation consistency was 7/10, and both the training balance accuracy and test balance accuracy were above 50%, and the model was statistically significant (χ2 = 7.633, P = 0.006). The interaction dendrogram of rs467021 and rs364051 was approached by red branches, indicating that rs467021 and rs364051 had a strong synergistic interaction on EH in Guizhou populations. Similarly, CpG_11, CpG_19 and CpG_20, CpG_11, CpG_19 and rs364051 interacted with EH in Guizhou population (χ2 = 15.575, P < 0.001 and χ2 = 19.874, P < 0.001) (Table 8 and Fig. 5).

Table 8 MDR analysis of SNPs and CpGs
Full size table
Fig. 5
figure 5

Different types of interaction dendrogram for SNPs and CpG sites. A SNP-SNP interaction in the general populations of Guizhou (n = 678). B SNP-SNP interaction in the Han population (n = 221). C CpG-CpG interaction in the Guizhou populations (n = 119). D SNP-CpG interaction in the Guizhou populations (n = 119). The dendrogram placed factors with strong interactions on the leaves. The color of the branches indicates the interaction from strong to weak (red, orange, green and blue). Red represents the highest degree of interaction or synergy, and blue represents low interaction or redundancy

Full size image

Discussion

Hypertension is a multifactorial disease associated with both the environment and heredity [15]. Guizhou province is located in southwest China, with a subtropical monsoon climate, more rainfall, humid climate, and high air humidity. It is a cosmopolitan province, with Miao and Buyi being the two ethnic minorities having the largest population. Its unique geographical environment promotes the dietary preferences for sour, smoked, spicy, oil, and wine. Thus, investigating the genetic susceptibility of EH in different ethnic groups in Guizhou by a case–control method is of great importance. The MassARRAY flight mass spectrometry method is a technology that detects gene molecular weight with high accuracy, sensitivity, and throughput [16].

The APP gene is located in the 21q21.3 region of human chromosomes, with a full length of 290,579 bp, containing 20 exons, and encoding the amyloid precursor protein. APP is a transmembrane protein continuously cleaved by β and γ secretases (amyloid pathway) to produce polypeptides including Aβ40 and Aβ42 [8, 17]. Specifically, Aβ42 is prone to misfolding and forming aggregates [18]. APP gene mutations cause the occurrence of the amyloid pathway and increase Aβ production and aggregation [9]. Aβ aggregation causes abnormal cerebrovascular metabolism, increases angiotensin II and cerebrovascular resistance, and subsequently induces cerebrovascular dysfunction, resulting in decreased cerebral blood flow. To maintain cerebral perfusion in the face of these metabolic abnormalities, cerebral perfusion pressure must be increased, resulting in systemic hypertension. Meanwhile, Aβ40 causes the production of reactive oxygen species and/or downregulation of nitric oxide synthase through NADPH oxidase, which mediates an increase in sympathetic nerve activity, thereby increasing the total peripheral resistance and hypertension occurrence [10]. Thus, our case–control study for the first time explored the relationship between APP gene SNP mutation and hypertension. Consequently, we found that rs2211772 is associated with EH in the Guizhou Han population. Studies indicate that rs2211772 is associated with cholesterol and high-density lipoprotein [19], and plasma cholesterol level is a risk factor for cardiovascular disease [20], corroborating our findings. rs2211772 (chr21: 26027126, T > C) is an intronic variant of CpG-SNP, which generates a CpG site. Introns increase transcript levels by influencing the transcription rate, nuclear export, transcript stability, and mRNA translation efficiency [21]. Studies have shown that intronic SNP variants promote mRNA transcription, resulting in epigenetic gene modification [22, 23]. By predicting the transcription factors bound by the sequence where the SNP is located, we found that the sequence has several transcription factor binding sites including TBX19. It has been shown that TBX19 is a transcription factor that regulates growth and development as well as blood pressure [24]. Therefore, the binding activity of rs2211772 genotypes should be investigated using chromatin immunoprecipitation (ChIP) assay and the function of rs2211772 polymorphism needs to be explored using the luciferase reporter assay.

Lynn M Bekris reported that rs2040273 minor allele carriers have significantly lower levels of CSF Aβ42 [25]. Similarly, our study noted that the distribution of alleles and genotypes of rs2040273 in the Guizhou Miao population had a small statistical P value (P = 0.051 and P = 0.095) between the disease group and the control group. Moreover, regression analysis showed that in contrast with allele A, hypertension risk in carriers of minor allele G decreased (OR 0.533, 95%CI 0.294–0.965, P = 0.038). Thus, the relationship between rs2040273 and hypertension in a larger population is worth studying.

Elsewhere, Craig Myrum performed a functional evaluation of rs2830077 and found that the SNP is located in the active region of chromatin, which may have transcriptional enhancer activity and is a binding site for transcription factor CP2. Luciferase analysis revealed that the expression of its allele C improves APP expression [26]. However, we did not identify the relationship between rs2830077 and hypertension. rs63750921 mutation changes the encoded amino acid, and the pathological examination of the patient displayed severe cerebral amyloid angiopathy. This suggests that rs63750921 has vascular tropism [27]. This study, which for the first time reports rs63750921 mutation in a Chinese population, did not identify gene mutation of rs63750921. This indicates that the SNP is significantly conservative among the Chinese population.

Polygenic diseases including hypertension and diabetes often do not follow the common Mendelian inheritance pattern, where one gene modifies the phenotype of another gene, causing complex higher-order interactions between two or more genes [28]. Therefore, genetic interactions may induce hypertension risk. Our gene interaction analysis revealed that the interaction model made up of rs467021 and rs364051 had a cross-validation consistency of 7/10 (P = 0.006), and the interaction line was red. This indicates that rs467021 and rs364051 have a strong positive interaction effect on EH in the Guizhou populations, confirming the above standpoint.

Although human genome-wide association research has identified a large number of genetic loci associated with hypertension, these loci account for only a small fraction of its heritability [29]. Epigenetic modifications may partly explain the genetic absence of hypertension [6]. The APP promoter has multiple possible transcription factor binding sites [30, 31]. Promoter methylation has a strong correlation with transcriptional silencing of APP [32]. The APP proximal promoter region is crucial for cell-specific expression of the APP gene [33]. Herein, the methylation sequences (− 265 to − 742 bp) detected were predicted to contain 25 binding transcription factors, indicating that the target sequence promotes transcriptional regulation. Furthermore, we found that CpG_10 (− 406 bp), CpG_19 (− 613 bp), and CpG_1 (− 296 bp) of the target sequence were associated with hypertension. Regression analysis adjusted for confounding factors, and showed that for every 1% increase in CpG_10 methylation level, hypertension risk decreased by 32.4%; every 1% increase in CpG_19 methylation level was associated with a 4.1% higher risk of hypertension; every increase in CpG_1 methylation level 1%, the risk of hypertension in women reduced by 8%. This may be attributed to changes in methylation levels, which trigger changes in the sequence of transcription factor binding sites, hence affecting APP gene expression [34, 35] and abnormal metabolism of APP, ultimately resulting in hypertension [10].

Genetic variation potentially modulates DNA methylation [36]. SNP and CpG site methylation may jointly promote gene expression or alternative splicing, providing novel insights into polygenic disease research [37, 38]. This study included three SNPs in the positive regulatory region of the APP gene promoter, i.e., rs466433 (− 875 bp/T > C, generating a new CpG site), rs364048 (− 953 bp/A > C), rs364051 (− 1158/ A > C) [39]. Previous studies indicate that the transcriptional activity of haplotype TA (rs466433–rs364048) in neural cells is four times higher than that of haplotype CG, and APP mutation promoter upregulates APP gene expression and aggravates Aβ accumulation [40, 41]. Although we did not identify the relationship between promoter SNP variants and hypertension, the carriers of the minor allele of the promoter SNP in the hypertensive populations significantly reduced the CpG_19 methylation levels. Additionally, the results of MDR interaction analysis showed that CpG_11, CpG_19, and the promoter variant rs364051 interacted with EH in Guizhou populations. Thus, the variation of the APP gene promoter may influence gene expression by targeting the methylation level, hence changing the blood pressure.

Although hypertension and AD are closely related, the mechanism responsible for the association is not clear [42]. Animal experiments indicate that hypertension activates receptors for advanced glycation end products (RAGE) in the cerebrovascular system via oxidative stress, and mediates the transcytosis of Aβ across brain endothelial cells, resulting in Aβ accumulation, cognitive impairment, and memory degradation [43]. Previous research findings have also pointed out that hypertension promotes APP processing, which may be a mechanism of pathogenic interaction between hypertension and AD [44]. In this work, the polymorphism and methylation of the APP gene, closely related to AD, were associated with hypertension. This provides a genetic reference for further research on the interaction mechanism between hypertension and AD.

This work has the following limitations. First, due to sampling limitations, we did not identify cholesterol, triglyceride, and high-density lipoprotein, among other biochemical indicators contributing to hypertension for the Miao and Buyi populations, and therefore, these populations were not be included in the model for regression analysis. Secondly, environmental data, including smoking and diet, were lacking, and therefore, we could not analyze the interaction between genes and the environment. As such, the association of APP gene polymorphism and promoter methylation with EH deserves to be studied in a larger population with more comprehensive indicators.

In conclusion, we used Sequenom MassARRAY to investigate the associations of APP gene rs2040273, rs63750921, rs2211772, rs2830077, rs467021, rs368196, rs466433, rs364048, rs364051, rs438031, rs463946, and promoter methylation with EH in Guizhou populations. For the first time, we found that the APP gene rs2211772 and promoter methylation levels may be associated with EH among the Guizhou populations. Our findings provide an important reference value for a deeper understanding of genetic pathogenesis, prevention, and control strategies of EH in Guizhou.

Materials and methods

Subjects

This work adopted the group design method of simple random sampling. The outpatient, inpatient, and healthy physical examination population of the Affiliated Hospital of Guizhou Medical University and township health centers in Leishan and Libo counties were selected as the research objects between 2016 and 2018. In Guizhou's Miao and Buyi counties (Leishan County and Libo County), two townships and two villages in each township were randomly selected for a health quality survey. For patients with abnormal blood pressure in the survey, follow-up re-measurement was not less than 2 times. Eventually, 343 patients with EH were selected, including 110 Miao, 119 Buyi, and 114 Guiyang Han; and 335 healthy controls, including 111 Miao, 117 Buyi, and 107 Guiyang Han. At the same time, 60 patients with EH and 59 healthy controls were selected for methylation level detection. This study was approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University, with the Approval Number: [2014] Lun Shen No. 45. All subjects voluntarily participated in the study and signed informed consent.

The inclusion criteria for the hypertension group were as follows: (1) meet the criteria of the 2010 Chinese Guidelines for the Prevention and Treatment of Hypertension: (2) age ≥ 18 years; blood pressure measured three times on different days, resting systolic blood pressure ≥ 140 mmHg (1 mm Hg = 0.133 kPa) and/or diastolic blood pressure ≥ 90 mmHg; (3) patients diagnosed with EH and currently taking antihypertensive drugs with normotensive blood pressure. All study subjects were Han, Miao, and Buyi people living in Guizhou, with no history of interracial marriage within three generations. Patients with secondary hypertension, congenital heart disease, cardiomyopathy, valvular disease, liver and kidney failure, pregnant women, substance abuse, or a history of mental illness were excluded.

The inclusion criteria for the control group are as follows. (1) systolic blood pressure < 140 mmHg and diastolic blood pressure < 90 mmHg; (2) no history of hypertension; (3) no antihypertensive drugs; all study subjects were Han, Miao, and Buyi people living in Guizhou, with no history of interracial marriage within three generations. Exclusion criteria were similar to that of the hypertension group.

Basic information collection

All research subjects recorded basic information including nation, gender, age, blood pressure, height, and weight, and were calculated based on the “Guidelines for the Prevention and Control of Overweight and Obesity in Chinese Adults”; the body mass index (BMI) formula (BMI = kg/m2) divided the study subjects into low BMI (BMI < 18.5 kg/m2), normal BMI (18.5 kg/m2 ≤ BMI < 24.0 kg/m2), and overweight BMI (24.0 kg/m2 ≤ BMI < 28.0 kg/m2), BMI obesity (BMI ≥ 28.0 kg/m2).

DNA extraction and quantification

A human peripheral blood DNA extraction kit (QIAGEN, Germany) was used to extract DNA from EDTA anticoagulated venous blood of the research subjects, and the concentration and purity were determined by NanoDrop2000 (Thermo Fisher Scientific Inc.). SNP detection requirements included: DNA concentration > 20 ng/μl, A260/A280 between 1.6 and 2.2; methylation detection requirements: DNA concentration > 20 ng/μl, A260/A280 between 1.6 and 2.2. DNA samples that did not meet the requirements were discarded.

SNP determination and primer design and synthesis

In the dbSNP database [45], the SNP sites were identified in the exon, promoter, and 3-UTR regions, and function prediction website [46] was used to predict the function of SNPs. We also screened the Ensembl database [47] to select SNPs which minor allele frequencies (MAF) in the Han Chinese in southern China (CHS) that were greater than 5% as primary screening SNPs. In addition, the susceptible SNPs associated with increased Aβ aggregation or AD were annotated by consulting the PubMed database [48]. The UCSC database [49] was used to confirm the genomic homology of the gene sequence, and the location of SNPs, and to assess the potential risk of genotyping. Assay Designer 4.0 (Agena Bioscience, Inc) was used to evaluate the primer design of multiple SNPs, and the design parameters were adjusted based on different SNP information to meet the optimization criteria. Using the PAGE primer purification method, three primers corresponding to each SNP were synthesized, including two PCR primers and one single-base extension primer (Table 9).

Table 9 Primer sequences for APP gene SNP genotyping and methylation detection
Full size table

Determination of methylated region and primer design and synthesis

The UCSC database was used to identify the promoter region of the APP gene, and the target sequence is located at chr21:26,171,035–26,171,512 (GRCh38/hg38), covering 478 bp, including 12 detectable CpG sites (Fig. 6). Transcription factors are proteins that bind DNA in a sequence-specific manner and regulate transcription, controlling chromatin and transcription by recognizing specific DNA sequences to form complex systems that direct genome expression. Potential transcription factors and binding sites of the target sequence were identified through the Promo database [50], including 25 predicted transcription factors associated with the expression of the APP gene, with positive or negative regulatory relationships. Primer protocol design for targeting sequences was performed using Agena EpiDesigner (Agena Bioscience, Inc.). PCR primer sequences of the corresponding fragments were synthesized using the PAGE primer purification method (Table 9).

Fig. 6
figure 6

Schematic diagram of APP gene promoter CpG site. Location of CpG sites: chr21:26,171,035–26,171,512 (GRCh38/hg38). Detectable CpG sites were marked in yellow. Undetectable CpG sites were marked in gray. CpG_3, CpG_4 and CpG_5, CpG_6 and CpG_16, CpG_8 and CpG_9 were detected as a unit, respectively

Full size image

Genotyping and methylation testing

PCR amplification reaction, shrimp alkaline phosphatase reaction, single-base extension reaction (for SNP detection) or transcriptase cleavage reaction (for methylation detection), resin purification, chip spotting, and mass spectrometry detection were sequentially performed using the MassARRAY detection platform. The TYPER 4.0 (Agena Bioscience, Inc) was used to collect the original data, genotyping map, and other test results. The sequences of CpG sites between two bases A were digested to obtain small fragments with similar molecular weight, then these CpG sites were combined and detected, and the result was the average methylation degree of these CpG sites. Therefore, CpG_3, CpG_4, and CpG_5; CpG_6 and CpG_16; CpG_8 and CpG_9 were detected as a unit.

Statistical analysis

Statistical analyses were performed using the SPSS 26.0 software (IBM Corp., Armonk, NY, USA). Normal measurement data were expressed as mean ± standard deviation (mean ± sd), and comparison between groups was performed by the Student's t test; skewed measurement data were represented by [Median, IQR (P25, P75)], and comparison between groups was performed by Mann–Whitney test. The count data were expressed as frequency (constituent ratio), and the comparison between groups was performed using the Chi-square test or Fisher's exact test. Binary logistic regression was used to analyze the relationship between SNPs and EH. Allele and genotype frequencies and the Hardy–Weinberg equilibrium test were calculated using the SNPStats online software [51]. Linkage disequilibrium analysis was performed using the Haploview 4.2 software. The SHEsis online software [52] was used to construct haplotypes and calculate the odds ratio (OR) and 95% confidence interval (CI). SNP-SNP, CpG-CpG, and CpG-SNP interaction analyses were performed using the MDR3.0.2 software. Methylation results were plotted using the R software (version 3.6.2, R Foundation for Statistical Computing, Vienna, Austria). A two-sided test P < 0.05 indicated a statistically significant difference.

Abbreviations

EH:

Essential hypertension

APP:

Amyloid precursor protein

SNP:

Single-nucleotide polymorphism

Aβ:

β-Amyloid

AD:

Alzheimer's disease

BMI:

Body mass index

OR:

Odds ratio

95%CI:

95% Confidence interval

MAF:

Minor Allele frequencies

References

  1. Zhang M, Shi Y, Zhou B, Huang Z, Zhao Z, Li C, et al. Prevalence, awareness, treatment, and control of hypertension in China, 2004–18: findings from six rounds of a national survey. BMJ (Clin Res Ed). 2023;380:e071952.

    Google Scholar 

  2. Tanira MO, Al Balushi KA. Genetic variations related to hypertension: a review. J Hum Hypertens. 2005;19(1):7–19.

    Article  CAS  PubMed  Google Scholar 

  3. Padmanabhan S, Dominiczak AF. Genomics of hypertension: the road to precision medicine. Nat Rev Cardiol. 2021;18(4):235–50.

    Article  CAS  PubMed  Google Scholar 

  4. Wang J, Gong L, Tan Y, Hui R, Wang Y. Hypertensive epigenetics: from DNA methylation to microRNAs. J Hum Hypertens. 2015;29(10):575–82.

    Article  CAS  PubMed  Google Scholar 

  5. Stoll S, Wang C, Qiu H. DNA methylation and histone modification in hypertension. Int J Mol Sci. 2018;19(4):1174.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Millis RM. Epigenetics and hypertension. Curr Hypertens Rep. 2011;13(1):21–8.

    Article  CAS  PubMed  Google Scholar 

  7. Kato N, Loh M, Takeuchi F, Verweij N, Wang X, Zhang W, et al. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nat Genet. 2015;47(11):1282–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Vassar R, Bennett BD, Babu-Khan S, Kahn S, Mendiaz EA, Denis P, et al. Beta-secretase cleavage of Alzheimer’s amyloid precursor protein by the transmembrane aspartic protease BACE. Science. 1999;286(5440):735–41.

    Article  CAS  PubMed  Google Scholar 

  9. Di Fede G, Catania M, Morbin M, Rossi G, Suardi S, Mazzoleni G, et al. A recessive mutation in the APP gene with dominant-negative effect on amyloidogenesis. Science. 2009;323(5920):1473–7.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tayler HM, Palmer JC, Thomas TL, Kehoe PG, Paton JF, Love S. Cerebral Aβ(40) and systemic hypertension. J Cereb Blood Flow Metab. 2018;38(11):1993–2005.

    Article  CAS  PubMed  Google Scholar 

  11. Ledoux S, Nalbantoglu J, Cashman NR. Amyloid precursor protein gene expression in neural cell lines: influence of DNA cytosine methylation. Brain Res Mol Brain Res. 1994;24(1–4):140–4.

    Article  CAS  PubMed  Google Scholar 

  12. Iwata A, Nagata K, Hatsuta H, Takuma H, Bundo M, Iwamoto K, et al. Altered CpG methylation in sporadic Alzheimer’s disease is associated with APP and MAPT dysregulation. Hum Mol Genet. 2014;23(3):648–56.

    Article  CAS  PubMed  Google Scholar 

  13. Abu Hamdeh S, Ciuculete DM, Sarkisyan D, Bakalkin G, Ingelsson M, Schiöth HB, et al. Differential DNA methylation of the genes for amyloid precursor protein, tau, and neurofilaments in human traumatic brain injury. J Neurotrauma. 2021;38(12):1679–88.

    Article  PubMed  Google Scholar 

  14. Li YY, Chen T, Wan Y, Xu SQ. Lead exposure in pheochromocytoma cells induces persistent changes in amyloid precursor protein gene methylation patterns. Environ Toxicol. 2012;27(8):495–502.

    Article  CAS  PubMed  Google Scholar 

  15. Faconti L, McNally RJ, Farukh B, Adeyemi O, Cruickshank JK, Wilkinson IB, et al. Differences in hypertension phenotypes between Africans and Europeans: role of environment. J Hypertens. 2020;38(7):1278–85.

    Article  CAS  PubMed  Google Scholar 

  16. Jurinke C, van den Boom D, Cantor CR, Köster H. The use of MassARRAY technology for high throughput genotyping. Adv Biochem Eng Biotechnol. 2002;77:57–74.

    CAS  PubMed  Google Scholar 

  17. Funamoto S, Tagami S, Okochi M, Morishima-Kawashima M. Successive cleavage of β-amyloid precursor protein by γ-secretase. Semin Cell Dev Biol. 2020;105:64–74.

    Article  CAS  PubMed  Google Scholar 

  18. Gu L, Guo Z. Alzheimer’s Aβ42 and Aβ40 peptides form interlaced amyloid fibrils. J Neurochem. 2013;126(3):305–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kathiresan S, Manning AK, Demissie S, D’Agostino RB, Surti A, Guiducci C, et al. A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study. BMC Med Genet. 2007;8(Suppl 1):S17.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kim MK, Han K, Kim HS, Park YM, Kwon HS, Yoon KH, et al. Cholesterol variability and the risk of mortality, myocardial infarction, and stroke: a nationwide population-based study. Eur Heart J. 2017;38(48):3560–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Shaul O. How introns enhance gene expression. Int J Biochem Cell Biol. 2017;91(Pt B):145–55.

    Article  CAS  PubMed  Google Scholar 

  22. Kuznetsova IL, Ponomareva NV, Alemastseva EA, Manakhov AD, Andreeva TV, Gusev FE, et al. The interactive effect of genetic and epigenetic variations in FKBP5 and ApoE genes on anxiety and brain EEG parameters. Genes. 2022;13(2):164.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Klengel T, Mehta D, Anacker C, Rex-Haffner M, Pruessner JC, Pariante CM, et al. Allele-specific FKBP5 DNA demethylation mediates gene-childhood trauma interactions. Nat Neurosci. 2013;16(1):33–41.

    Article  CAS  PubMed  Google Scholar 

  24. Ghosh TK, Brook JD, Wilsdon A. T-Box genes in human development and disease. Curr Top Dev Biol. 2017;122:383–415.

    Article  CAS  PubMed  Google Scholar 

  25. Bekris LM, Tsuang DW, Peskind ER, Yu CE, Montine TJ, Zhang J, et al. Cerebrospinal fluid Aβ42 levels and APP processing pathway genes in Parkinson’s disease. Mov Disord. 2015;30(7):936–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Myrum C, Nikolaienko O, Bramham CR, Haavik J, Zayats T. Implication of the APP gene in intellectual abilities. J Alzheimers Dis. 2017;59(2):723–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Obici L, Demarchi A, de Rosa G, Bellotti V, Marciano S, Donadei S, et al. A novel AbetaPP mutation exclusively associated with cerebral amyloid angiopathy. Ann Neurol. 2005;58(4):639–44.

    Article  CAS  PubMed  Google Scholar 

  28. Stanfill AG, Starlard-Davenport A. Primer in genetics and genomics, article 7-multifactorial concepts: gene–gene interactions. Biol Res Nurs. 2018;20(3):359–64.

    Article  PubMed  Google Scholar 

  29. Ehret GB, Caulfield MJ. Genes for blood pressure: an opportunity to understand hypertension. Eur Heart J. 2013;34(13):951–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Vostrov AA, Taheny MJ, Quitschke WW. A region to the N-terminal side of the CTCF zinc finger domain is essential for activating transcription from the amyloid precursor protein promoter. J Biol Chem. 2002;277(2):1619–27.

    Article  CAS  PubMed  Google Scholar 

  31. Querfurth HW, Jiang J, Xia W, Selkoe DJ. Enhancer function and novel DNA binding protein activity in the near upstream betaAPP gene promoter. Gene. 1999;232(1):125–41.

    Article  CAS  PubMed  Google Scholar 

  32. Mani ST, Thakur MK. In the cerebral cortex of female and male mice, amyloid precursor protein (APP) promoter methylation is higher in females and differentially regulated by sex steroids. Brain Res. 2006;1067(1):43–7.

    Article  CAS  PubMed  Google Scholar 

  33. Lahiri DK, Ge YW. Role of the APP promoter in Alzheimer’s disease: cell type-specific expression of the beta-amyloid precursor protein. Ann N Y Acad Sci. 2004;1030:310–6.

    Article  CAS  PubMed  Google Scholar 

  34. Medvedeva YA, Khamis AM, Kulakovskiy IV, Ba-Alawi W, Bhuyan MS, Kawaji H, et al. Effects of cytosine methylation on transcription factor binding sites. BMC Genomics. 2014;15:119.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Cox LA, Nathanielsz PW. The importance of altered gene promoter methylation and transcription factor binding in developmental programming of central appetitive drive. J Physiol. 2009;587(Pt 20):4763–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lu YH, Wang BH, Jiang F, Mo XB, Wu LF, He P, et al. Multi-omics integrative analysis identified SNP-methylation-mRNA: Interaction in peripheral blood mononuclear cells. J Cell Mol Med. 2019;23(7):4601–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Soto-Ramírez N, Arshad SH, Holloway JW, Zhang H, Schauberger E, Ewart S, et al. The interaction of genetic variants and DNA methylation of the interleukin-4 receptor gene increase the risk of asthma at age 18 years. Clin Epigenetics. 2013;5(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Bell JT, Pai AA, Pickrell JK, Gaffney DJ, Pique-Regi R, Degner JF, et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 2011;12(1):R10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lahiri DK. Functional characterization of amyloid beta precursor protein regulatory elements: rationale for the identification of genetic polymorphism. Ann N Y Acad Sci. 2004;1030:282–8.

    Article  CAS  PubMed  Google Scholar 

  40. Lv H, Jia L, Jia J. Promoter polymorphisms which modulate APP expression may increase susceptibility to Alzheimer’s disease. Neurobiol Aging. 2008;29(2):194–202.

    Article  CAS  PubMed  Google Scholar 

  41. Theuns J, Brouwers N, Engelborghs S, Sleegers K, Bogaerts V, Corsmit E, et al. Promoter mutations that increase amyloid precursor-protein expression are associated with Alzheimer disease. Am J Hum Genet. 2006;78(6):936–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Abdulrahman H, van Dalen JW, den Brok M, Latimer CS, Larson EB, Richard E. Hypertension and Alzheimer’s disease pathology at autopsy: a systematic review. Alzheimer’s Dement. 2022. https://doi.org/10.1002/alz.12707.

    Article  Google Scholar 

  43. Carnevale D, Mascio G, D’Andrea I, Fardella V, Bell RD, Branchi I, et al. Hypertension induces brain β-amyloid accumulation, cognitive impairment, and memory deterioration through activation of receptor for advanced glycation end products in brain vasculature. Hypertension. 2012;60(1):188–97.

    Article  CAS  PubMed  Google Scholar 

  44. Faraco G, Park L, Zhou P, Luo W, Paul SM, Anrather J, et al. Hypertension enhances Aβ-induced neurovascular dysfunction, promotes β-secretase activity, and leads to amyloidogenic processing of APP. J Cereb Blood Flow Metab. 2016;36(1):241–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. The dbSNP database. https://www.ncbi.nlm.nih.gov/snp/. Accessed 4 Aug 2022.

  46. The function prediction website. http://snpinfo.niehs.nih.gov/. Accessed 4 Aug 2022.

  47. The Ensembl database. https://asia.ensembl.org/index.html. Accessed 4 Aug 2022.

  48. The pubmed database. https://pubmed.ncbi.nlm.nih.gov/. Accessed 4 Aug 2022.

  49. The UCSC database. http://genome.ucsc.edu/. Accessed 4 Aug 2022.

  50. The Promo database. http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3/. Accessed 4 Aug 2022.

  51. SNPStats online software. https://snpstats.net/start.htm. Accessed 4 Aug 2022.

  52. SHEsis online software. http://analysis.bio-x.cn/. Accessed 4 Aug 2022.

Download references

Funding

Funding was provided by Natural Science Foundation of China (Grant Nos. 31560306, U1812403), the Guizhou Science and Technology Support Project, Social Development Field in the form of a grant [(2019) 2807], and The project of Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education, Guizhou Medical University (Grant No. FZSW-2022-002).

Author information

Author notes

  1. Ruichao Li and Juhui Song have contributed equally to this work

Authors and Affiliations

  1. Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, & Collaborative Innovation Center for Prevention and Control of Endemic and Ethnic Regional Diseases Co-constructed by the Province and Ministry, Guizhou Medical University, Guiyang, 550004, Guizhou, China

    Ruichao Li, Juhui Song, Ansu Zhao, Ting Zhang, Xiaolan Qi, Zhizhong Guan, Chanjuan Wang & Yan He

  2. Department of Cardiovascular Medicine, Affiliated Hospital of Guizhou Medical University, Guiyang, China

    Xiaoyan Diao

  3. The Clinical Laboratory Center, Guizhou Provincial People’s Hospital, Guiyang, China

    Yu An

  4. Antenatal Diagnosis Centre, Guizhou Provincial People’s Hospital, Guiyang, China

    Lingyan Ren

Authors
  1. Ruichao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juhui Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ansu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoyan Diao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaolan Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhizhong Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yu An
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Lingyan Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Chanjuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yan He
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RL performed the study design, detected SNPs genotyping and methylation of APP gene, and was a major contributor in writing the manuscript. JS performed the detection of SNPs genotyping and methylation of APP gene, and wrote the main manuscript text. AZ performed the analysis of data. XD and TZ acquired high blood pressure and control samples. XQ, ZG, and LR contributed to the conception or design of the work. CW and YA analyzed and interpreted the data of SNPs and methylation. YH contributed to the conception or design of the study and revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Lingyan Ren, Chanjuan Wang or Yan He.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

APP allele and genotype distribution.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, R., Song, J., Zhao, A. et al. Association of APP gene polymorphisms and promoter methylation with essential hypertension in Guizhou: a case–control study. Hum Genomics 17, 25 (2023). https://doi.org/10.1186/s40246-023-00462-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40246-023-00462-y

Keywords

Human Genomics

ISSN: 1479-7364