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CC BY-NC-ND 4.0 · Thromb Haemost 2022; 122(06): 1027-1039
DOI: 10.1055/s-0041-1742169
Stroke, Systemic or Venous Thromboembolism

Integrated GWAS and Gene Expression Suggest ORM1 as a Potential Regulator of Plasma Levels of Cell-Free DNA and Thrombosis Risk

Sonia Lopez 
1   Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
,
Angel Martinez-Perez 
1   Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
,
Alba Rodriguez-Rius
1   Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
,
Ana Viñuela
2   Biosciences Institute, Faculty of Medicine, Newcastle University, Newcastle Upon Tyne, United Kingdom
,
Andrew A. Brown
3   Population Health and Genomics, University of Dundee, Dundee, Scotland, United Kingdom
,
Laura Martin-Fernandez
1   Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
4   Fundación Española de Trombosis y Hemostasia (FETH), Madrid, Spain
5   Congenital Coagulopathies Laboratory, Banc de Sang i Teixits, Barcelona, Spain
6   Transfusional Medicine, Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona (VHIR-UAB), Barcelona, Spain
,
Noelia Vilalta
7   Haemostasis and Thrombosis Unit, Department of Hematology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
,
Marc Arús
7   Haemostasis and Thrombosis Unit, Department of Hematology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
,
Nikolaos I. Panousis
8   Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, South Cambridgeshire, United Kingdom
9   Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland
,
Alfonso Buil
10   Institute of Biological Psychiatry, Mental Health Sct. Hans Hospital, Roskilde, Denmark
,
Maria Sabater-Lleal
1   Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
11   Cardiovascular Medicine Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
,
Juan Carlos Souto
7   Haemostasis and Thrombosis Unit, Department of Hematology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
,
Emmanouil T. Dermitzakis
9   Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland
,
Jose Manuel Soria
1   Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
› Author Affiliations Funding This study was supported by grants of the Spanish Government Instituto de Salud Carlos III and Fondo de Investigación Sanitaria (ISCIII-FIS) (PI14/00582, PI17/00059, J.M.S. and S.L.), Grupo Consolidado Generalitat de Catalunya (SGR 1736, J.M.S.), CERCA Programme/Generalitat de Catalunya, Fundación Española de Trombosis y Hemostasia (FETH; L.M-.F.), and the nonprofit association Activa'TT por la Salud. A.R-.R. was supported by a predoctoral fellowship of the Catalonia Government Agència de Gestió d'ajuts Universitaris i de Recerca (AGAUR) (FI2017B_00673). M.S-.L. is supported by a Miguel Servet contract from the ISCIII (CP17/00142) and co-financed by the European Social Fund.
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Abstract

Plasma cell-free DNA (cfDNA) is a surrogate marker of neutrophil extracellular traps (NETs) that contribute to immunothrombosis. There is growing interest about the mechanisms underlying NET formation and elevated cfDNA, but little is known about the factors involved. We aimed to identify genes involved in the regulation of cfDNA levels using data from the Genetic Analysis of Idiopathic Thrombophilia (GAIT-2) Project.

Imputed genotypes, whole blood RNA-Seq data, and plasma cfDNA quantification were available for 935 of the GAIT-2 participants from 35 families with idiopathic thrombophilia. We performed heritability and GWAS analysis for cfDNA. The heritability of cfDNA was 0.26 (p = 3.7 × 10−6), while the GWAS identified a significant association (rs1687391, p = 3.55 × 10−10) near the ORM1 gene, on chromosome 9. An eQTL (expression quantitative trait loci) analysis revealed a significant association between the lead GWAS variant and the expression of ORM1 in whole blood (p = 6.14 × 10−9). Additionally, ORM1 expression correlated with levels of cfDNA (p = 4.38 × 10−4). Finally, genetic correlation analysis between cfDNA and thrombosis identified a suggestive association (ρ g = 0.43, p = 0.089).

All in all, we show evidence of the role of ORM1 in regulating cfDNA levels in plasma, which might contribute to the susceptibility to thrombosis through mechanisms of immunothrombosis.

Keywords

immunothrombosis - neutrophil extracellular traps - α-1-acid glycoprotein 1 - expression quantitative trait loci

Ethical Approval

The study was performed in compliance with the Helsinki Declaration. The Ethical Committee at Hospital de la Santa Creu i Sant Pau approved the GAIT-2 study. Adult participants provided informed consent for themselves and for their minor children.


Supplementary Material



Publication History

Received: 17 March 2021

Accepted: 20 October 2021

Article published online:
10 March 2022

© 2022. 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/)

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  • References

  • 1 Aucamp J, Bronkhorst AJ, Badenhorst CPS, Pretorius PJ. The diverse origins of circulating cell-free DNA in the human body: a critical re-evaluation of the literature. Biol Rev Camb Philos Soc 2018; 93 (03) 1649-1683
    CrossrefPubMedGoogle Scholar
  • 2 Pös O, Biró O, Szemes T, Nagy B. Circulating cell-free nucleic acids: characteristics and applications. Eur J Hum Genet 2018; 26 (07) 937-945
    CrossrefPubMedGoogle Scholar
  • 3 Brinkmann V, Reichard U, Goosmann C. et al. Neutrophil extracellular traps kill bacteria. Science 2004; 303 (5663): 1532-1535
    CrossrefPubMedGoogle Scholar
  • 4 Denning N-L, Aziz M, Gurien SD, Wang P. DAMPs and NETs in sepsis. Front Immunol 2019; 10: 2536
    CrossrefPubMedGoogle Scholar
  • 5 Demers M, Wagner DD. NETosis: a new factor in tumor progression and cancer-associated thrombosis. Semin Thromb Hemost 2014; 40 (03) 277-283
    Article in Thieme ConnectPubMedGoogle Scholar
  • 6 Lee KH, Kronbichler A, Park DD-Y. et al. Neutrophil extracellular traps (NETs) in autoimmune diseases: a comprehensive review. Autoimmun Rev 2017; 16 (11) 1160-1173
    CrossrefPubMedGoogle Scholar
  • 7 Döring Y, Soehnlein O, Weber C. Neutrophil extracellular traps in atherosclerosis and atherothrombosis. Circ Res 2017; 120 (04) 736-743
    CrossrefPubMedGoogle Scholar
  • 8 Zawrotniak M, Rapala-Kozik M. Neutrophil extracellular traps (NETs) - formation and implications. Acta Biochim Pol 2013; 60 (03) 277-284
    CrossrefPubMedGoogle Scholar
  • 9 Saffarzadeh M, Juenemann C, Queisser MA. et al. Neutrophil extracellular traps directly induce epithelial and endothelial cell death: a predominant role of histones. PLoS One 2012; 7 (02) e32366
    CrossrefPubMedGoogle Scholar
  • 10 Kimball AS, Obi AT, Diaz JA, Henke PK. The emerging role of NETs in venous thrombosis and immunothrombosis. Front Immunol 2016; 7: 236
    CrossrefPubMedGoogle Scholar
  • 11 Fuchs TA, Brill A, Duerschmied D. et al. Extracellular DNA traps promote thrombosis. Proc Natl Acad Sci U S A 2010; 107 (36) 15880-15885
    CrossrefPubMedGoogle Scholar
  • 12 von Brühl M-L, Stark K, Steinhart A. et al. Monocytes, neutrophils, and platelets cooperate to initiate and propagate venous thrombosis in mice in vivo. J Exp Med 2012; 209 (04) 819-835
    CrossrefPubMedGoogle Scholar
  • 13 Schulz C, Engelmann B, Massberg S. Crossroads of coagulation and innate immunity: the case of deep vein thrombosis. J Thromb Haemost 2013; 11 (Suppl. 01) 233-241
    CrossrefPubMedGoogle Scholar
  • 14 van Montfoort ML, Stephan F, Lauw MN. et al. Circulating nucleosomes and neutrophil activation as risk factors for deep vein thrombosis. Arterioscler Thromb Vasc Biol 2013; 33 (01) 147-151
    CrossrefPubMedGoogle Scholar
  • 15 Diaz JA, Fuchs TA, Jackson TO. et al; for the Michigan Research Venous Group*. Plasma DNA is elevated in patients with deep vein thrombosis. J Vasc Surg Venous Lymphat Disord 2013; 1 (04) 341.e1-348.e1
    PubMedGoogle Scholar
  • 16 Brill A, Fuchs TA, Savchenko AS. et al. Neutrophil extracellular traps promote deep vein thrombosis in mice. J Thromb Haemost 2012; 10 (01) 136-144
    CrossrefPubMedGoogle Scholar
  • 17 Jiménez-Alcázar M, Limacher A, Panda R. et al. Circulating extracellular DNA is an independent predictor of mortality in elderly patients with venous thromboembolism. PLoS One 2018; 13 (02) e0191150
    CrossrefPubMedGoogle Scholar
  • 18 Middleton EA, He XY, Denorme F. et al. Neutrophil extracellular traps contribute to immunothrombosis in COVID-19 acute respiratory distress syndrome. Blood 2020; 136 (10) 1169-1179
    CrossrefPubMedGoogle Scholar
  • 19 Zuo Y, Yalavarthi S, Shi H. et al. Neutrophil extracellular traps in COVID-19. JCI Insight 2020; 5 (11) e138999
    PubMedGoogle Scholar
  • 20 Jylhävä J, Lyytikäinen L-P, Kähönen M. et al. A genome-wide association study identifies UGT1A1 as a regulator of serum cell-free DNA in young adults: the cardiovascular risk in young Finns study. PLoS One 2012; 7 (04) e35426
    CrossrefPubMedGoogle Scholar
  • 21 Martin-Fernandez L, Ziyatdinov A, Carrasco M. et al. Genetic determinants of thrombin generation and their relation to venous thrombosis: results from the GAIT-2 Project. PLoS One 2016; 11 (01) e0146922
    CrossrefPubMedGoogle Scholar
  • 22 Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 1988; 16 (03) 1215
    CrossrefPubMedGoogle Scholar
  • 23 Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009; 25 (14) 1754-1760
    CrossrefPubMedGoogle Scholar
  • 24 Ziyatdinov A, Brunel H, Martinez-Perez A, Buil A, Perera A, Soria JM. solarius: an R interface to SOLAR for variance component analysis in pedigrees. Bioinformatics 2016; 32 (12) 1901-1902
    CrossrefPubMedGoogle Scholar
  • 25 Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 1998; 62 (05) 1198-1211
    CrossrefPubMedGoogle Scholar
  • 26 Self SG, Liang K-Y. Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. J Am Stat Assoc 1987; 82 (398) 605-610
    CrossrefPubMedGoogle Scholar
  • 27 Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 2012; 28 (10) 1353-1358
    CrossrefPubMedGoogle Scholar
  • 28 Pruim RJ, Welch RP, Sanna S. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 2010; 26 (18) 2336-2337
    CrossrefPubMedGoogle Scholar
  • 29 Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 2016; 32 (10) 1479-1485
    CrossrefPubMedGoogle Scholar
  • 30 Huang DW, Sherman BT, Tan Q. et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 2007; 8 (09) R183
    CrossrefPubMedGoogle Scholar
  • 31 Joehanes R, Zhang X, Huan T. et al. Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. Genome Biol 2017; 18 (01) 16
    CrossrefPubMedGoogle Scholar
  • 32 Stegelmeier AA, Darzianiazizi M, Hanada K. et al. Type I interferon-mediated regulation of antiviral capabilities of neutrophils. Int J Mol Sci 2021; 22 (09) 4726
    CrossrefPubMedGoogle Scholar
  • 33 Sun BB, Maranville JC, Peters JE. et al. Genomic atlas of the human plasma proteome. Nature 2018; 558 (7708): 73-79
    CrossrefPubMedGoogle Scholar
  • 34 Carty CL, Keene KL, Cheng Y-C. et al; COMPASS and METASTROKE Consortia. Meta-analysis of genome-wide association studies identifies genetic risk factors for stroke in African Americans. Stroke 2015; 46 (08) 2063-2068
    CrossrefPubMedGoogle Scholar
  • 35 Astle WJ, Elding H, Jiang T. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 2016; 167 (05) 1415-1429
    CrossrefPubMedGoogle Scholar
  • 36 Kichaev G, Bhatia G, Loh P-R. et al. Leveraging polygenic functional enrichment to improve GWAS power. Am J Hum Genet 2019; 104 (01) 65-75
    CrossrefPubMedGoogle Scholar
  • 37 Williams FMK, Carter AM, Hysi PG. et al; EuroCLOT Investigators, Wellcome Trust Case Control Consortium 2, MOnica Risk, Genetics, Archiving and Monograph, MetaStroke, International Stroke Genetics Consortium. Ischemic stroke is associated with the ABO locus: the EuroCLOT study. Ann Neurol 2013; 73 (01) 16-31
    CrossrefPubMedGoogle Scholar
  • 38 Wadelius M, Chen LY, Eriksson N. et al. Association of warfarin dose with genes involved in its action and metabolism. Hum Genet 2007; 121 (01) 23-34
    CrossrefPubMedGoogle Scholar
  • 39 Rocanin-Arjo A, Cohen W, Carcaillon L. et al; CardioGenics Consortium. A meta-analysis of genome-wide association studies identifies ORM1 as a novel gene controlling thrombin generation potential. Blood 2014; 123 (05) 777-785
    CrossrefPubMedGoogle Scholar
  • 40 Sabater-Lleal M, Ji Y, Temprano G. et al. Genome-wide association analyses of natural anticoagulants using TOPMED reference panel reveal novel loci associated with Antithrombin, Protein C and Protein S. (Abstract #2131). Paper presented at: The 70th Annual Meeting of The American Society of Human Genetics. October 27–30, 2020. Virtual Meeting
    PubMedGoogle Scholar
  • 41 Sai K, Kurose K, Koizumi T. et al. Distal promoter regions are responsible for differential regulation of human orosomucoid-1 and -2 gene expression and acute phase responses. Biol Pharm Bull 2014; 37 (01) 164-168
    CrossrefPubMedGoogle Scholar
  • 42 Fournier T, Medjoubi-N N, Porquet D. Alpha-1-acid glycoprotein. Biochim Biophys Acta 2000; 1482 (1–2): 157-171
    CrossrefPubMedGoogle Scholar
  • 43 Luo Z, Lei H, Sun Y, Liu X, Su D-F. Orosomucoid, an acute response protein with multiple modulating activities. J Physiol Biochem 2015; 71 (02) 329-340
    CrossrefPubMedGoogle Scholar
  • 44 Irmak S, Oliveira-Ferrer L, Singer BB, Ergün S, Tilki D. Pro-angiogenic properties of orosomucoid (ORM). Exp Cell Res 2009; 315 (18) 3201-3209
    CrossrefPubMedGoogle Scholar
  • 45 de Vries B, Walter SJ, Wolfs TGAM. et al. Exogenous alpha-1-acid glycoprotein protects against renal ischemia-reperfusion injury by inhibition of inflammation and apoptosis. Transplantation 2004; 78 (08) 1116-1124
    CrossrefPubMedGoogle Scholar
  • 46 Boncela J, Papiewska I, Fijalkowska I, Walkowiak B, Cierniewski CS. Acute phase protein alpha 1-acid glycoprotein interacts with plasminogen activator inhibitor type 1 and stabilizes its inhibitory activity. J Biol Chem 2001; 276 (38) 35305-35311
    CrossrefPubMedGoogle Scholar
  • 47 Osikov MV, Makarov EV, Krivokhizhina LV. Effects of α1-acid glycoprotein on hemostasis in experimental septic peritonitis. Bull Exp Biol Med 2007; 144 (02) 178-180
    CrossrefPubMedGoogle Scholar
  • 48 Liu J, Marey MA, Kowsar R. et al. An acute-phase protein as a regulator of sperm survival in the bovine oviduct: alpha 1-acid-glycoprotein impairs neutrophil phagocytosis of sperm in vitro. J Reprod Dev 2014; 60 (05) 342-348
    CrossrefPubMedGoogle Scholar
  • 49 Zhang D, Huang J, Luo D, Feng X, Liu Y, Liu Y. Glycosylation change of alpha-1-acid glycoprotein as a serum biomarker for hepatocellular carcinoma and cirrhosis. Biomarkers Med 2017; 11 (05) 423-430
    CrossrefPubMedGoogle Scholar
  • 50 Berntsson J, Östling G, Persson M, Smith JG, Hedblad B, Engström G. Orosomucoid, carotid plaque, and incidence of stroke. Stroke 2016; 47 (07) 1858-1863
    CrossrefPubMedGoogle Scholar
  • 51 Barr TL, Conley Y, Ding J. et al. Genomic biomarkers and cellular pathways of ischemic stroke by RNA gene expression profiling. Neurology 2010; 75 (11) 1009-1014
    CrossrefPubMedGoogle Scholar
  • 52 Szpechcinski A, Struniawska R, Zaleska J. et al. Evaluation of fluorescence-based methods for total vs. amplifiable DNA quantification in plasma of lung cancer patients. J Physiol Pharmacol 2008; 59 (Suppl. 06) 675-681
    PubMedGoogle Scholar