CC BY-NC-ND 4.0 · Thromb Haemost 2021; 121(05): 573-583
DOI: 10.1055/s-0040-1720980
Coagulation and Fibrinolysis

Comparison of DNA Methylation Profiles of Hemostatic Genes between Liver Tissue and Peripheral Blood within Individuals

1   Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
2   Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
,
Annelie Angerfors*
1   Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
3   Bioinformatics Core Facility, University of Gothenburg, Gothenburg, Sweden
,
Björn Andersson
3   Bioinformatics Core Facility, University of Gothenburg, Gothenburg, Sweden
,
Staffan Nilsson
1   Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
4   Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
,
Marcela Davila Lopez
3   Bioinformatics Core Facility, University of Gothenburg, Gothenburg, Sweden
,
Lena Hansson
5   NovoNordisk, Oxford, United Kingdom
,
Tara M. Stanne**
1   Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
2   Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
,
Christina Jern**
1   Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
2   Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
› Institutsangaben
Funding This study was supported by the Swedish Heart and Lung Foundation (20190203), the Swedish Research Council (2018-02543), the Swedish state under the agreement between the Swedish government and the county councils (the ALF-agreement, ALFGBG-720081), the Bioinformatics Long-term Support (WABI), SciLifeLab (Stockholm and Uppsala, Sweden), the Swedish Foundation for Strategic Research (RIF14–0081), the Rune and Ulla Amlövs Foundation for Neurologic Research, the John and Brit Wennerström Foundation for Neurologic Research, the Marcus Borgströms Foundation for Neurologic Research, and the Nilsson-Ehle Endowments.

Abstract

DNA methylation has become increasingly recognized in the etiology of complex diseases, including thrombotic disorders. Blood is often collected in epidemiological studies for genotyping and has recently also been used to examine DNA methylation in epigenome-wide association studies. DNA methylation patterns are often tissue-specific, thus, peripheral blood may not accurately reflect the methylation pattern in the tissue of relevance. Here, we collected paired liver and blood samples concurrently from 27 individuals undergoing liver surgery. We performed targeted bisulfite sequencing for a set of 35 hemostatic genes primarily expressed in liver to analyze DNA methylation levels of >10,000 cytosine-phosphate-guanine (CpG) dinucleotides. We evaluated whether DNA methylation in blood could serve as a proxy for DNA methylation in liver at individual CpGs. Approximately 30% of CpGs were nonvariable and were predominantly hypo- (<25%) or hypermethylated (>70%) in both tissues. While blood can serve as a proxy for liver at these CpGs, the low variability renders these unlikely to explain phenotypic differences. We therefore focused on CpG sites with variable methylation levels in liver. The level of blood–liver tissue correlation varied widely across these variable CpGs; moderate correlations (0.5 ≤ r < 0.75) were detected for 6% and strong correlations (r ≥ 0.75) for a further 4%. Our findings indicate that it is essential to study the concordance of DNA methylation between blood and liver at individual CpGs. This paired blood–liver dataset is intended as a resource to aid interpretation of blood-based DNA methylation results.

Authors' Contributions

M.O.L., T.M.S., and C.J. conceived the research design of the present study. C.J. provided funding and was responsible for sample contribution. M.O.L. isolated gDNA and prepared sequencing libraries. M.O.L., L.H., and M.D.L. acquisitioned and processed the data. A.A., B.A., and S.N. performed the statistical analyses. M.O.L., A.A., and B.A. drafted the figures. M.O.L., A.A., C.J., and T.M.S. interpreted the data. M.O.L., C.J., and T.M.S. drafted the manuscript. All authors intellectually reviewed the manuscript, contributed to the last revision process, and approved the version to be published.


* These authors contributed equally to this work.


** These authors jointly supervised this work.


Supplementary Material



Publikationsverlauf

Eingereicht: 12. März 2020

Angenommen: 03. Oktober 2020

Artikel online veröffentlicht:
17. November 2020

© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 de Vries PS, Chasman DI, Sabater-Lleal M. et al. A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration. Hum Mol Genet 2016; 25 (02) 358-370
  • 2 Olsson M, Stanne TM, Pedersen A. et al. Genome-wide analysis of genetic determinants of circulating factor VII-activating protease (FSAP) activity. J Thromb Haemost 2018; 16 (10) 2024-2034
  • 3 Smith NL, Chen MH, Dehghan A. et al. Wellcome Trust Case Control Consortium. Novel associations of multiple genetic loci with plasma levels of factor VII, factor VIII, and von Willebrand factor: the CHARGE (Cohorts for Heart and Aging Research in Genome Epidemiology) Consortium. Circulation 2010; 121 (12) 1382-1392
  • 4 Stanne TM, Olsson M, Lorentzen E. et al. A Genome-wide study of common and rare genetic variants associated with circulating thrombin activatable fibrinolysis inhibitor. Thromb Haemost 2018; 118 (02) 298-308
  • 5 Nikpay M, Goel A, Won HH. et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet 2015; 47 (10) 1121-1130
  • 6 Malik R, Chauhan G, Traylor M. et al. AFGen Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, International Genomics of Blood Pressure (iGEN-BP) Consortium, INVENT Consortium, STARNET, BioBank Japan Cooperative Hospital Group, COMPASS Consortium, EPIC-CVD Consortium, EPIC-InterAct Consortium, International Stroke Genetics Consortium (ISGC), METASTROKE Consortium, Neurology Working Group of the CHARGE Consortium, NINDS Stroke Genetics Network (SiGN), UK Young Lacunar DNA Study, MEGASTROKE Consortium. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet 2018; 50 (04) 524-537
  • 7 Klarin D, Emdin CA, Natarajan P, Conrad MF, Kathiresan S. INVENT Consortium. Genetic analysis of venous thromboembolism in UK Biobank identifies the ZFPM2 locus and implicates obesity as a causal risk factor. Circ Cardiovasc Genet 2017; 10 (02) e001643
  • 8 Falcone GJ, Woo D. Genetics of spontaneous intracerebral hemorrhage. Stroke 2017; 48 (12) 3420-3424
  • 9 Konkle BA, Johnsen JM, Wheeler M, Watson C, Skinner M, Pierce GF. My Life Our Future programme. Genotypes, phenotypes and whole genome sequence: approaches from the My Life Our Future haemophilia project. Haemophilia 2018; 24 (Suppl. 06) 87-94
  • 10 Do C, Shearer A, Suzuki M. et al. Genetic-epigenetic interactions in cis: a major focus in the post-GWAS era. Genome Biol 2017; 18 (01) 120
  • 11 Davis Armstrong NM, Chen WM, Brewer MS. et al. Epigenome-wide analyses identify two novel associations with recurrent stroke in the Vitamin Intervention for Stroke Prevention clinical trial. Front Genet 2018; 9: 358
  • 12 Krupinski J, Carrera C, Muiño E. et al. DNA methylation in stroke. Update of latest advances. Comput Struct Biotechnol J 2017; 16: 1-5
  • 13 Nakatochi M, Ichihara S, Yamamoto K. et al. Epigenome-wide association of myocardial infarction with DNA methylation sites at loci related to cardiovascular disease. Clin Epigenetics 2017; 9: 54
  • 14 Rask-Andersen M, Martinsson D, Ahsan M. et al. Epigenome-wide association study reveals differential DNA methylation in individuals with a history of myocardial infarction. Hum Mol Genet 2016; 25 (21) 4739-4748
  • 15 Fernández-Sanlés A, Sayols-Baixeras S, Curcio S, Subirana I, Marrugat J, Elosua R. DNA methylation and age-independent cardiovascular risk, an epigenome-wide approach: the REGICOR study (REgistre GIroni del COR). Arterioscler Thromb Vasc Biol 2018; 38 (03) 645-652
  • 16 Agha G, Mendelson MM, Ward-Caviness CK. et al. Blood leukocyte DNA methylation predicts risk of future myocardial infarction and coronary heart disease. Circulation 2019; 140 (08) 645-657
  • 17 Byun HM, Siegmund KD, Pan F. et al. Epigenetic profiling of somatic tissues from human autopsy specimens identifies tissue- and individual-specific DNA methylation patterns. Hum Mol Genet 2009; 18 (24) 4808-4817
  • 18 Lokk K, Modhukur V, Rajashekar B. et al. DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns. Genome Biol 2014; 15 (04) r54
  • 19 Varley KE, Gertz J, Bowling KM. et al. Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res 2013; 23 (03) 555-567
  • 20 Jiang R, Jones MJ, Chen E. et al. Discordance of DNA methylation variance between two accessible human tissues. Sci Rep 2015; 5: 8257
  • 21 Slieker RC, Bos SD, Goeman JJ. et al. Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array. Epigenetics Chromatin 2013; 6 (01) 26
  • 22 Braun PR, Han S, Hing B. et al. Genome-wide DNA methylation comparison between live human brain and peripheral tissues within individuals. Transl Psychiatry 2019; 9 (01) 47
  • 23 Walton E, Hass J, Liu J. et al. Correspondence of DNA methylation between blood and brain tissue and its application to schizophrenia research. Schizophr Bull 2016; 42 (02) 406-414
  • 24 Edgar RD, Jones MJ, Meaney MJ, Turecki G, Kobor MS. BECon: a tool for interpreting DNA methylation findings from blood in the context of brain. Transl Psychiatry 2017; 7 (08) e1187
  • 25 Hewitt AW, Januar V, Sexton-Oates A. et al. DNA methylation landscape of ocular tissue relative to matched peripheral blood. Sci Rep 2017; 7: 46330
  • 26 Olsson Lindvall M, Hansson L, Klasson S, Davila Lopez M, Jern C, Stanne TM. Hemostatic genes exhibit a high degree of allele-specific regulation in liver. Thromb Haemost 2019; 119 (07) 1072-1083
  • 27 Olsson Lindvall M, Davila Lopez M, Klasson S. et al. A comprehensive sequencing-based analysis of allelic methylation patterns in hemostatic genes in human liver. Thromb Haemost 2020; 120 (02) 229-242
  • 28 GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet 2013; 45 (06) 580-585
  • 29 Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9 (04) 357-359
  • 30 Kersey PJ, Allen JE, Armean I. et al. Ensembl Genomes 2016: more genomes, more complexity. Nucleic Acids Res 2016; 44 (D1): D574-D580
  • 31 Cheung WA, Shao X, Morin A. et al. Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome. Genome Biol 2017; 18 (01) 50
  • 32 Hannon E, Lunnon K, Schalkwyk L, Mill J. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics 2015; 10 (11) 1024-1032
  • 33 Smith AK, Kilaru V, Kocak M. et al. Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type. BMC Genomics 2014; 15: 145
  • 34 Lin D, Chen J, Perrone-Bizzozero N. et al. Characterization of cross-tissue genetic-epigenetic effects and their patterns in schizophrenia. Genome Med 2018; 10 (01) 13
  • 35 Jjingo D, Conley AB, Yi SV, Lunyak VV, Jordan IK. On the presence and role of human gene-body DNA methylation. Oncotarget 2012; 3 (04) 462-474
  • 36 Folsom AR, Wu KK, Rosamond WD, Sharrett AR, Chambless LE. Prospective study of hemostatic factors and incidence of coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) Study. Circulation 1997; 96 (04) 1102-1108
  • 37 Meade TW, Mellows S, Brozovic M. et al. Haemostatic function and ischaemic heart disease: principal results of the Northwick Park Heart Study. Lancet 1986; 2 (8506): 533-537
  • 38 Tracy RP, Arnold AM, Ettinger W, Fried L, Meilahn E, Savage P. The relationship of fibrinogen and factors VII and VIII to incident cardiovascular disease and death in the elderly: results from the cardiovascular health study. Arterioscler Thromb Vasc Biol 1999; 19 (07) 1776-1783
  • 39 Zakai NA, Lange L, Longstreth Jr WT. et al. Association of coagulation-related and inflammation-related genes and factor VIIc levels with stroke: the Cardiovascular Health Study. J Thromb Haemost 2011; 9 (02) 267-274
  • 40 Friso S, Lotto V, Choi SW. et al. Promoter methylation in coagulation F7 gene influences plasma FVII concentrations and relates to coronary artery disease. J Med Genet 2012; 49 (03) 192-199