Pharmacoepigenetics of hypertension: genome-wide methylation analysis of responsiveness to four classes of antihypertensive drugs using a double-blind crossover study design

Marja-Liisa Nuotio, Heini Sánez Tähtisalo, Alexandra Lahtinen, Kati Donner, Frej Fyhrquist, Markus Perola, Kimmo K Kontula, Timo P Hiltunen, Marja-Liisa Nuotio, Heini Sánez Tähtisalo, Alexandra Lahtinen, Kati Donner, Frej Fyhrquist, Markus Perola, Kimmo K Kontula, Timo P Hiltunen

Abstract

Essential hypertension remains the leading risk factor of global disease burden, but its treatment goals are often not met. We investigated whether DNA methylation is associated with antihypertensive responses to a diuretic, a beta-blocker, a calcium channel blocker or an angiotensin receptor antagonist. In addition, since we previously showed an SNP at the transcription start site (TSS) of the catecholamine biosynthesis-related ACY3 gene to associate with blood pressure (BP) response to beta-blockers, we specifically analysed the association of methylation sites close to the ACY3 TSS with BP responses to beta-blockers. We conducted an epigenome-wide association study between leukocyte DNA methylation and BP responses to antihypertensive monotherapies in two hypertensive Finnish cohorts: the GENRES (https://ichgcp.net/clinical-trials-registry/NCT03276598; amlodipine 5 mg, bisoprolol 5 mg, hydrochlorothiazide 25 mg, or losartan 50 mg daily) and the LIFE-Fin studies (https://ichgcp.net/clinical-trials-registry/NCT00338260; atenolol 50 mg or losartan 50 mg daily). The monotherapy groups consisted of approximately 200 individuals each. We identified 64 methylation sites to suggestively associate (P < 1E-5) with either systolic or diastolic BP responses to a particular study drug in GENRES. These associations did not replicate in LIFE-Fin . Three methylation sites close to the ACY3 TSS were associated with systolic BP responses to bisoprolol in GENRES but not genome-wide significantly (P < 0.05). No robust associations between DNA methylation and BP responses to four different antihypertensive drugs were identified. However, the findings on the methylation sites close to the ACY3 TSS may support the role of ACY3 genetic and epigenetic variation in BP response to bisoprolol.

Keywords: Epigenomics; hypertension; pharmacogenetics; precision medicine.

Conflict of interest statement

The authors report no conflict of interest.

Figures

Figure 1.
Figure 1.
Scatter plots for the most significant correlations between normalized methylation degrees (M-values) and covariate-adjusted blood pressure responses in GENRES. Results from the EWAS analysis are shown as effect sizes and P values. A linear regression line is displayed.
Figure 2.
Figure 2.
P-values for the associations of methylation sites across ACY3 with blood pressure response to bisoprolol in the GENRES Study. Black boxes on the transcript schemes represent coding exons, and white boxes non-coding exons. Lines between boxes depict introns. For methylation (CpG) site numbering (on the abscissa) and precise genomic positioning, see Table 3

References

    1. GBD 2019 . Risk factors collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1223–1249.
    1. Beaney T, Schutte AE, Stergiou GS, et al. May measurement month 2019: the global blood pressure screening campaign of the international society of hypertension. Hypertension. 2020;76:333–341.
    1. Turner ST, Boerwinkle E, O’Connell JR, et al. Genomic association analysis of common variants influencing antihypertensive response to hydrochlorothiazide. Hypertension. 2013;62:391–397.
    1. Frau F, Zaninello R, Salvi E, et al. Genome-wide association study identifies CAMKID variants involved in blood pressure response to losartan: the SOPHIA study. Pharmacogenomics. 2014;15:1643–1652.
    1. Hiltunen TP, Donner KM, Sarin AP, et al. Pharmacogenomics of hypertension: a genome‐wide, placebo‐controlled cross‐over study, using four classes of antihypertensive drugs. J Am Heart Assoc. 2015;4:e001521.
    1. Chittani M, Zaninello R, Lanzani C, et al. TET2 and CSMD1 genes affect SBP response to hydrochlorothiazide in never-treated essential hypertensives. J Hypertens. 2015;33:1301–1309.
    1. Gong Y, McDonough CW, Beitelshees AL, et al. PTPRD gene associated with blood pressure response to atenolol and resistant hypertension. J Hypertens. 2015;33:2278–2285.
    1. Salvi E, Wang Z, Rizzi F, et al. Genome-wide and gene-based meta-analyses identify novel loci influencing blood pressure response to hydrochlorothiazide. Hypertension. 2017;69:51–59.
    1. Singh S, Warren HR, Hiltunen TP, et al. Genome-wide meta-analysis of blood pressure response to β1-blockers: results from ICAPS (International Consortium of Antihypertensive Pharmacogenomics Studies). J Am Heart Assoc. 2019;8:e013115.
    1. Evangelou E, Warren HR, Mosen-Ansorena D, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50:1412–1425.
    1. Sánez Tähtisalo H, Ruotsalainen S, Mars N, et al. Human essential hypertension: no significant association of polygenic risk scores with antihypertensive drug responses. Sci Rep. 2020;10:11940.
    1. Cowley AW Jr, Nadeau JH, Baccarelli A, et al. Report of the national heart, lung, and blood institute working group on epigenetics and hypertension. Hypertension. 2012;59:899–905.
    1. Allis CD, Jenuwein T.. The molecular hallmarks of epigenetic control. Nat Rev Genet. 2016;17:487–500.
    1. Feinberg AP. The key role of epigenetics in human disease prevention and mitigation. N Engl J Med. 2018;378:1323–1334.
    1. Dor Y, Cedar H. Principles of DNA methylation and their implications for biology and medicine. Lancet. 2018;392:777–786.
    1. Hiltunen TP, Suonsyrjä T, Hannila-Handelberg T, et al. Predictors of antihypertensive drug responses: initial data from a placebo-controlled, randomized, cross-over study with four antihypertensive drugs (The GENRES Study). Am J Hypertens. 2007;20:311–318.
    1. Rimpelä JM, Kontula KK, Fyhrquist F, et al. Replicated evidence for aminoacylase 3 and nephrin gene variations to predict antihypertensive drug responses. Pharmacogenomics. 2017;18:445–458.
    1. Dahlöf B, Devereux RB, Kjeldsen SE, et al. Cardiovascular morbidity and mortality in the Losartan Intervention For Endpoint reduction in hypertension study (LIFE): a randomised trial against atenolol. Lancet. 2002;359:995–1003.
    1. Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363–1369.
    1. R Core Team . R: a language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing; 2016.
    1. Maksimovic J, Gordon L, Oshlack A. SWAN: subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol. 2012;13:R44.
    1. Chen YA, Lemire M, Choufani S, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8:203–209.
    1. Chen EY, Tan CM, Kou Y, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128.
    1. Kuleshov MV, Jones MR, Rouillard AD, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44:W90–97.
    1. Xie Z, Bailey A, and Kuleshov MV, et al. Gene set knowledge discovery with Enrichr. Curr Protoc. 2021;1. e90.
    1. Houseman EA, Accomando WP, Koestler DC, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.
    1. Rimpelä JM, Pörsti IH, Jula A, et al. Genome-wide association study of nocturnal blood pressure dipping in hypertensive patients. BMC Med Genet. 2018;19:110.
    1. Newman D, Abuladze N, Scholz K, et al. Specificity of aminoacylase III-mediated deacetylation of mercapturic acids. Drug Metab Dispos. 2007;35:43–50.
    1. Liang M. Epigenetic mechanisms and hypertension. Hypertension. 2018;72:1244–1254.
    1. Stoll S, Wang C, Qiu H. DNA methylation and histone modification in hypertension. Int J Mol Sci. 2018;19:1174.
    1. Gonzalez-Jaramillo V, Portilla-Fernandez E, Glisic M, et al. The role of DNA methylation and histone modifications in blood pressure: a systematic review. J Hum Hypertens. 2019;33:703–715.
    1. Irvin MR, Jones AC, Claas SA, et al. DNA methylation and blood pressure phenotypes: a review of the literature. Am J Hypertens. 2021;34:267–273.
    1. Kato N, Loh M, Takeuchi F, 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;47:1282–1293.
    1. Richard MA, Huan T, Ligthart S, et al. DNA methylation analysis identifies loci for blood pressure regulation. Am J Hum Genet. 2017;101:888–902.
    1. Kazmi N, Elliott HR, Burrows K, et al. Associations between high blood pressure and DNA methylation. PLoS One. 2020;15:e0227728.
    1. Huang Y, Ollikainen M, Muniandy M, et al. Identification, heritability, and relation with gene expression of novel DNA methylation loci for blood pressure. Hypertension. 2020;76:195–205.
    1. Ge S, Wang Y, Song M, et al. Type 2 diabetes mellitus: integrative analysis of multiomics data for biomarker discovery. OMICS. 2018;22:514–523.
    1. Ouni M, Saussenthaler S, Eichelmann F, et al. Epigenetic changes in islets of Langerhans preceding the onset of diabetes. Diabetes. 2020;69:2503–2517.
    1. Ruggieri A, Saredi S, Zanotti S, et al. DNAJB6 myopathies: focused review on an emerging and expanding group of myopathies. Front Mol Biosci. 2016;3:63.
    1. Al-Barghouthi BM, Mesner LD, Calabrese GM, et al. Systems genetics in diversity outbred mice inform BMD GWAS and identify determinants of bone strength. Nat Commun. 2021;12:3408.
    1. Nixon TRW, Alexander P, Richards A, et al. Homozygous type IX collagen variants (COL9A1, COL9A2, and COL9A3) causing recessive Stickler syndrome - expanding the phenotype. Am J Med Genet A. 2019;179:1498–1506.
    1. Huang D, Deng X, Ma K, et al. Association of COL9A3 trp3 polymorphism with intervertebral disk degeneration: a meta-analysis. BMC Musculoskelet Disord. 2018;19:381.
    1. Suonsyrjä T, Hannila-Handelberg T, Paavonen KJ, et al. Laboratory tests as predictors of the antihypertensive effects of amlodipine, bisoprolol, hydrochlorothiazide and losartan in men: results from the randomized, double-blind, crossover GENRES Study. J Hypertens. 2008;26:1250–1256.
    1. Turner ST, Schwartz GL, Chapman AB, et al. Plasma renin activity predicts blood pressure responses to beta-blocker and thiazide diuretic as monotherapy and add-on therapy for hypertension. Am J Hypertens. 2010;23:1014–1022.
    1. Lin Q, Zhao G, Fang X, et al. IP3 receptors regulate vascular smooth muscle contractility and hypertension. JCI Insight. 2016;1:e89402.
    1. Eid AH, El-Yazbi AF, Zouein F, et al. Inositol 1,4,5-trisphosphate receptors in hypertension. Front Physiol. 2018;26:1018.
    1. Kichaev G, Bhatia G, Loh PR, et al. Leveraging polygenic functional enrichment to improve GWAS power. Am J Hum Genet. 2019;104:65–75.
    1. Schweda F, Kurtz L, de Wit C, et al. Substitution of connexin40 with connexin45 prevents hyperreninemia and attenuates hypertension. Kidney Int. 2009;75:482–489.
    1. Pushkin A, Carpenito G, Abuladze N, et al. Structural characterization, tissue distribution, and functional expression of murine aminoacylase III. Am J Physiol Cell Physiol. 2004;286:C848–856.
    1. Long PM, Stradecki HM, Minturn JE, et al. Differential aminoacylase expression in neuroblastoma. Int J Cancer. 2011;129:1322–1330.
    1. The Genotype-Tissue Expression (GTEx) Portal . The broad institute of MIT and harvard, Cambridge (MA). 2017. [cited 2021 Aug 21];

Source: PubMed

3
S'abonner