Abstract
Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification and a potential drug target for vascular calcific disease.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All data supporting the findings in this study are included in the main article and associated files. An overview of the TOPMed participant consents and data access procedures is provided at https://topmed.nhlbi.nih.gov/topmed-data-access-scientific-community. Participant-level genotype and phenotype data are available to approved investigators via the database of Genotypes and Phenotypes (dbGaP). The dbGaP accession numbers for all TOPMed studies referenced in this paper are listed in Supplementary Table 14. Additionally, genomic summary results pertaining to the pooled GWAS of CAC score are available at phs001974, as detailed at https://topmed.nhlbi.nih.gov/topmed-genomic-summary-results-public.
References
Kavousi, M. et al. Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Ann. Intern. Med. 156, 438–444 (2012).
Budoff, M. J. et al. Ten-year association of coronary artery calcium with atherosclerotic cardiovascular disease (ASCVD) events: the multi-ethnic study of atherosclerosis (MESA). Eur. Heart J. 39, 2401–2408 (2018).
Greenland, P., Blaha, M. J., Budoff, M. J., Erbel, R. & Watson, K. E. Coronary calcium score and cardiovascular risk. J. Am. Coll. Cardiol. 72, 434–447 (2018).
Polonsky, T. S. et al. Coronary artery calcium score and risk classification for coronary heart disease prediction. JAMA 303, 1610–1616 (2010).
Shi, X. et al. Calcification in atherosclerotic plaque vulnerability: friend or foe? Front. Physiol. 11, 56 (2020).
Malhotra, R. et al. HDAC9 is implicated in atherosclerotic aortic calcification and affects vascular smooth muscle cell phenotype. Nat. Genet. 51, 1580–1587 (2019).
Sutton, N. R. et al. Molecular mechanisms of vascular health: insights from vascular aging and calcification. Arterioscler. Thromb. Vasc. Biol. 43, 15–29 (2023).
Nikpay, M. et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).
Klarin, D. et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat. Genet. 49, 1392–1397 (2017).
Verweij, N., Eppinga, R. N., Hagemeijer, Y. & van der Harst, P. Identification of 15 novel risk loci for coronary artery disease and genetic risk of recurrent events, atrial fibrillation and heart failure. Sci. Rep. 7, 2761 (2017).
Howson, J. M. M. et al. Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms. Nat. Genet. 49, 1113–1119 (2017).
Koyama, S. et al. Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat. Genet. 52, 1169–1177 (2020).
Matsunaga, H. et al. Transethnic meta-analysis of genome-wide association studies identifies three new loci and characterizes population-specific differences for coronary artery disease. Circ. Genom. Precis. Med. 13, e002670 (2020).
Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet. 54, 1803–1815 (2022).
Natarajan, P. et al. Multiethnic exome-wide association study of subclinical atherosclerosis. Circ. Cardiovasc. Genet. 9, 511–520 (2016).
Wojczynski, M. K. et al. Genetics of coronary artery calcification among African Americans, a meta-analysis. BMC Med. Genet. 14, 75 (2013).
O’Donnell, C. J. et al. Genome-wide association study for coronary artery calcification with follow-up in myocardial infarction. Circulation 124, 2855–2864 (2011).
Bielak, L. F. & Peyser, P. A. Genetics of subclinical coronary atherosclerosis. Curr. Genet. Med. Rep. 6, 116–123 (2018).
Lo Sardo, V. et al. Unveiling the role of the most impactful cardiovascular risk locus through haplotype editing. Cell 175, 1796–1810 (2018).
Gupta, R. M. et al. A genetic variant associated with five vascular diseases is a distal regulator of endothelin-1 gene expression. Cell 170, 522–533 (2017).
Innerarity, T. L. et al. Familial defective apolipoprotein B-100: low density lipoproteins with abnormal receptor binding. Proc. Natl Acad. Sci. USA. 84, 6919–6923 (1987).
Mahley, R. W. Apolipoprotein E: cholesterol transport protein with expanding role in cell biology. Science 240, 622–630 (1988).
Aherrahrou, R., Aherrahrou, Z., Schunkert, H. & Erdmann, J. Coronary artery disease associated gene Phactr1 modulates severity of vascular calcification in vitro. Biochem. Biophys. Res. Commun. 491, 396–402 (2017).
Wellcome Trust Case Control Consortium et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).
Pereira, A. C. et al. Age, gender, and race-based coronary artery calcium score percentiles in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Clin. Cardiol. 39, 352–359 (2016).
Budoff, M. J. et al. Coronary artery and thoracic calcium on noncontrast thoracic CT scans: comparison of ungated and gated examinations in patients from the COPD Gene cohort. J. Cardiovasc. Comput. Tomogr. 5, 113–118 (2011).
Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).
Zhao, Q. et al. Molecular mechanisms of coronary disease revealed using quantitative trait loci for TCF21 binding, chromatin accessibility, and chromosomal looping. Genome Biol. 21, 135 (2020).
Durham, A. L., Speer, M. Y., Scatena, M., Giachelli, C. M. & Shanahan, C. M. Role of smooth muscle cells in vascular calcification: implications in atherosclerosis and arterial stiffness. Cardiovasc. Res. 114, 590–600 (2018).
Leopold, J. A. Vascular calcification: mechanisms of vascular smooth muscle cell calcification. Trends Cardiovasc. Med. 25, 267–274 (2015).
Nicholson, C. J. et al. Reversal of aging-induced increases in aortic stiffness by targeting cytoskeletal protein–protein interfaces. J. Am. Heart Assoc. 7, e008926 (2018).
Brunetti-Pierri, N. et al. X-linked recessive chondrodysplasia punctata: spectrum of arylsulfatase E gene mutations and expanded clinical variability. Am. J. Med. Genet. A 117A, 164–168 (2003).
Lin, M. E., Chen, T., Leaf, E. M., Speer, M. Y. & Giachelli, C. M. Runx2 expression in smooth muscle cells is required for arterial medial calcification in mice. Am. J. Pathol. 185, 1958–1969 (2015).
Sun, Y. et al. Smooth muscle cell-specific Runx2 deficiency inhibits vascular calcification. Circ. Res. 111, 543–552 (2012).
Uzui, H. et al. Increased expression of membrane type 3-matrix metalloproteinase in human atherosclerotic plaque: role of activated macrophages and inflammatory cytokines. Circulation 106, 3024–3030 (2002).
Itoh, Y. Membrane-type matrix metalloproteinases: their functions and regulations. Matrix Biol. 44–46, 207–223 (2015).
Rohwedder, I. et al. Plasma fibronectin deficiency impedes atherosclerosis progression and fibrous cap formation. EMBO Mol. Med. 4, 564–576 (2012).
Ding, H. T., Wang, C. G., Zhang, T. L. & Wang, K. Fibronectin enhances in vitro vascular calcification by promoting osteoblastic differentiation of vascular smooth muscle cells via ERK pathway. J. Cell. Biochem. 99, 1343–1352 (2006).
Watson, K. E., Parhami, F., Shin, V. & Demer, L. L. Fibronectin and collagen I matrixes promote calcification of vascular cells in vitro, whereas collagen IV matrix is inhibitory. Arterioscler. Thromb. Vasc. Biol. 18, 1964–1971 (1998).
Kuzuya, M. et al. Effect of MMP-2 deficiency on atherosclerotic lesion formation in ApoE-deficient mice. Arterioscler. Thromb. Vasc. Biol. 26, 1120–1125 (2006).
Wagsater, D., Zhu, C., Bjorkegren, J., Skogsberg, J. & Eriksson, P. MMP-2 and MMP-9 are prominent matrix metalloproteinases during atherosclerosis development in the Ldlr−/−Apob100/100 mouse. Int. J. Mol. Med. 28, 247–253 (2011).
Hecht, E. et al. The matrix metalloproteinases 2 and 9 initiate uraemic vascular calcifications. Nephrol. Dial. Transplant. 31, 789–797 (2016).
Jiang, L. et al. Calpain-1 regulation of matrix metalloproteinase 2 activity in vascular smooth muscle cells facilitates age-associated aortic wall calcification and fibrosis. Hypertension 60, 1192–1199 (2012).
Bailey, M. et al. Involvement of matrix metalloproteinases and tenascin-C in elastin calcification. Cardiovasc. Pathol. 13, 146–155 (2004).
Fang, H. et al. Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet. 105, 763–772 (2019).
Taliun, D. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
Agatston, A. S. et al. Quantification of coronary artery calcium using ultrafast computed tomography. J. Am. Coll. Cardiol. 15, 827–832 (1990).
Carr, J. J. et al. Calcified coronary artery plaque measurement with cardiac CT in population-based studies: standardized protocol of Multi-Ethnic Study of Atherosclerosis (MESA) and Coronary Artery Risk Development in Young Adults (CARDIA) study. Radiology 234, 35–43 (2005).
Stilp, A. A system for phenotype harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program. Am. J. Epidemiol. 190, 1977–1992 (2021).
Conomos, M. P., Miller, M. B. & Thornton, T. A. Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness. Genet. Epidemiol. 39, 276–293 (2015).
Bild, D. E. et al. Ethnic differences in coronary calcification: the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 111, 1313–1320 (2005).
Conomos, M. P., Reiner, A. P., Weir, B. S. & Thornton, T. A. Model-free estimation of recent genetic relatedness. Am. J. Hum. Genet. 98, 127–148 (2016).
Conomos, M. P. et al. Genetic diversity and association studies in US Hispanic/Latino populations: applications in the Hispanic Community Health Study/Study of Latinos. Am. J. Hum. Genet. 98, 165–184 (2016).
Sofer, T. et al. A fully adjusted two-stage procedure for rank-normalization in genetic association studies. Genet. Epidemiol. 43, 263–275 (2019).
Gogarten, S. M. et al. Genetic association testing using the GENESIS R/Bioconductor package. Bioinformatics 35, 5346–5348 (2019).
Chen, H. et al. Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. Am. J. Hum. Genet. 98, 653–666 (2016).
Dey, R., Schmidt, E. M., Abecasis, G. R. & Lee, S. A fast and accurate algorithm to test for binary phenotypes and its application to PheWAS. Am. J. Hum. Genet. 101, 37–49 (2017).
Zhou, W. et al. Efficiently controlling for case–control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).
Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).
Dong, C. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24, 2125–2137 (2015).
McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
Fishilevich, S. et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database (Oxford) 2017, bax028 (2017).
Zerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T. & Flicek, P. R. The ensembl regulatory build. Genome Biol. 16, 56 (2015).
Rogers, M. F. et al. FATHMM-XF: accurate prediction of pathogenic point mutations via extended features. Bioinformatics 34, 511–513 (2018).
Liu, X. et al. WGSA: an annotation pipeline for human genome sequencing studies. J. Med. Genet. 53, 111–112 (2016).
Chen, H. et al. Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing studies. Am. J. Hum. Genet. 104, 260–274 (2019).
Lee, S. et al. Optimal unified approach for rare-variant association testing with application to small-sample case–control whole-exome sequencing studies. Am. J. Hum. Genet. 91, 224–237 (2012).
Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).
Kelly, T. N. et al. Insights from a large-scale whole-genome sequencing study of systolic blood pressure, diastolic blood pressure, and hypertension. Hypertension 79, 1656–1667 (2022).
Selvaraj, M. S. et al. Whole genome sequence analysis of blood lipid levels in >66,000 individuals. Nat. Commun. 13, 5995 (2022).
Natarajan, P. et al. Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices. Nat. Commun. 12, 2182 (2021).
Wessel, J. et al. Rare non-coding variation identified by large scale whole genome sequencing reveals unexplained heritability of type 2 diabetes. Preprint at medRxiv https://doi.org/10.1101/2020.11.13.20221812 (2020).
1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
Chomczynski, P. & Sacchi, N. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal. Biochem. 162, 156–159 (1987).
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the \(2^{-\Delta\Delta C_{\rm{T}}}\) method. Methods 25, 402–408 (2001).
O’Rourke, C. et al. Calcification of vascular smooth muscle cells and imaging of aortic calcification and inflammation. J. Vis. Exp. 54017 (2016).
Kang, H. et al. Bone morphogenetic protein 4 promotes vascular smooth muscle contractility by activating microRNA-21 (miR-21), which down-regulates expression of family of dedicator of cytokinesis (DOCK) proteins. J. Biol. Chem. 287, 3976–3986 (2012).
Turner, A. W. et al. Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk. Nat. Genet. 54, 804–816 (2022).
Acknowledgements
This work was funded by National Heart, Lung, and Blood Institute (NHLBI) grant R01HL146860. P.S.d.V. and N.R.H. were additionally supported by American Heart Association grant 18CDA34110116. M.P.C. was supported by NHLBI grant U01HL137162. R. Malhotra was supported by the NHLBI (R01HL142809, R01HL159514 and R01HL162928), the American Heart Association (18TPA34230025) and the Wild Family Foundation. P.S.d.V. and R. Malhotra were supported by NHLBI grant R01 HL162928. T.N.K. was supported by NHLBI grant R01HL120393. G.P., M.S.S. and P.N. were supported by NHLBI grant R01HL142711. C.L.M. was supported by funding from the NHLBI (R01HL148239 and R01HL164577), Fondation Leducq ‘PlaqOmics’ (18CVD02), the Chan Zuckerberg Initiative and the Silicon Valley Community Foundation. Molecular data for the TOPMed program were supported by the NHLBI. See the TOPMed Omics Support Table (Supplementary Table 14) for study-specific omics support information. Core support, including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering, were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support, including phenotype harmonization, data management, sample identity quality control and general program coordination, were provided by the TOPMed Data Coordinating Center (R01HL-120393 and U01HL-120393; contract HHSN268201800001I). Study-specific funding is detailed in the Supplementary Methods. We thank L. A. Cupples for helpful comments that improved the manuscript. We acknowledge contributions from the NHLBI TOPMed program, the TOPMed Atherosclerosis Working Group, and the VA Million Veteran Program. Consortium members are listed in the Supplementary Methods. We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed.
Author information
Authors and Affiliations
Contributions
P.S.d.V., M.P.C., K.S., C.J.N., D.J., S.M.L., P.A.P., C.L.M. and R. Malhotra contributed to the study design and conception and to the drafting of the manuscript. P.S.d.V., K.S., C.J.N., D.J., J.C.B., L.F.B., S.L.C., J.G.T., N.D.P., L.R.Y., N.H.-C., X.S., A.M.S., A.K., A.B.N., B.I.F., B.G.K., C.P.M., D.R.J., D.A.N., E.B., F.F.W., G.H., G.L.K., H.V.D., J.B., J.D.C., J.G.W., J.A.S., J.D.S., K.A.V.-M., K.A.Y., L.A.L., L.S.E., M.F., Q.W., R.A.M., R.A.G., S.C.N., S.M.D., S.L.R.K., V.N., Y.-I.M., T.N.K., C.J.O.D., A.C.M., D.W.B., L.C.B., A.C., B.D.M., B.M.P., J.J.C., T.L.A., N.O.S., C.A.L., R.S.V., W.S.P., P.A.P. and R. Malhotra contributed to data collection and processing. P.S.d.V., M.P.C., K.S., C.J.N., D.J., N.R.H., D.W., X.G., J.Y., E.P.Y., C.T., A.T.H., M.R.B., S.M., S.L.C., J.W., M.S.S., X.S., G.J., J.V.M., M.Y., P.N., R. Mehran, V.F., V.N., W.Z., A.K.M., G.P., T.N.K., N.O.S., C.L.M., R.S.V., W.S.P. and R. Malhotra contributed to data analysis or interpretation. K.S., C.J.N., W.J., S.L., C.L.L.C., R.H.L., M.S.M., W.T., W.M.Z. and R. Malhotra conducted and interpreted the in vitro and histologic experiments. All authors critically reviewed and approved the manuscript.
Corresponding authors
Ethics declarations
Competing interests
B.P.M. serves on the Steering Committee of the Yale Open Data Access Project, funded by Johnson & Johnson. L.S.E. is now an employee of Celgene/Bristol Myers Squibb. Celgene/Bristol Myers Squibb had no role in the funding, design, conduct or interpretation of this study. N.O.S. has received research funding from Regeneron Pharmaceuticals, unrelated to this work. K.A.V.-M. is an employee at Illumina. R.W.K. is an employee at Psomagen. R. Mehran reports institutional research grants from Abbott Laboratories, Abiomed, Applied Therapeutics, AstraZeneca, Bayer, Beth Israel Deaconess, Bristol Myers Squibb, CERC, Chiesi, Concept Medical, CSL Behring, DSI, Medtronic, Novartis Pharmaceuticals and OrbusNeich; consultant fees from Abbott Laboratories, Boston Scientific, CardiaWave, Chiesi, Janssen Scientific Affairs, Medscape/WebMD, Medtelligence (Janssen Scientific Affairs), Roivant Sciences, Sanofi and Siemens Medical Solutions; consultant fees paid to the institution from Abbott Laboratories and Bristol Myers Squibb; advisory board fees paid to the institution from Spectranetics/Philips/Volcano Corp.; consultant fees (spouse) from Abiomed, The Medicines Company and Merck; equity <1% from Claret Medical and Elixir Medical; DSMB membership fees paid to the institution from Watermark Research Partners; consulting (no fee) from Idorsia Pharmaceuticals and Regeneron Pharmaceuticals; and Associate Editor for American College of Cardiology (ACC) and American Medical Association (AMA). R. Malhotra is a consultant for MyoKardia (now owned by Bristol Myers Squibb), Epizon Pharma, Renovacor and Third Pole; is a co-founder of Patch and Angea Biotherapeutics; and has received research funding from Angea Biotherapeutics, Bayer Pharmaceuticals and Amgen. A.M.S. receives funding from Seven Bridges Genomics to develop tools for the NHLBI BioData Catalyst consortium. P.N. reports grants from Amgen, Apple, Boston Scientific, AstraZeneca, Allelica, Novartis and Genentech; consulting income from GV, Blackstone Life Sciences, Foresite Labs, Apple, AstraZeneca, Allelica, Novartis, HeartFlow and Genentech; being a scientific advisor to Esperion Therapeutics, Preciseli and TenSixteen Bio; being a scientific co-founder of TenSixteen Bio; and spousal employment at Vertex, all unrelated to the present work. C.L.M. received a research grant from AstraZeneca for an unrelated project. The remaining authors declare no competing interests.
Peer review
Peer review information
Nature Cardiovascular Research thanks the anonymous reviewers for their contributions to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Coronary artery calcification levels across ARSE index variant genotypes suggest a recessive mode of inheritance.
Mean log (coronary artery calcification + 1) across genotypes for index variants at ARSE. Error bars indicate the 95% confidence interval for the mean (n = 22,400).
Extended Data Fig. 2 Co-localization of genetic associations with coronary artery calcification and genetic associations with ARSE expression in a) cultured fibroblasts and b) aorta.
These plots were created with Locuscomparer (https://github.com/boxiangliu/locuscomparer) using the African 1000G ph3 reference population to calculate the linkage disequilibrium r2. Unadjusted two-sided P values are provided. The posterior probability for causal variant sharing was 99.9% in cultured fibroblasts and 8.9% in aorta.
Extended Data Fig. 3
Co-localization of genetic associations with coronary artery calcification and genetic associations with MMP16 expression in aorta. This plot was created with Locuscomparer (https://github.com/boxiangliu/locuscomparer) using the European 1000G ph3 reference population to calculate the linkage disequilibrium r2. Unadjusted two-sided P values are provided. The posterior probability for causal variant sharing was 81.9%.
Extended Data Fig. 4 Cell type-specific gene expression of ARSE and MMP16 in an integrated human atherosclerosis reference dataset.
(a-b) Uniform Manifold Approximation and Projection (UMAP) embeddings from an integrated human carotid and coronary artery atherosclerosis single-cell RNA-seq reference dataset (Methods), showing (a) ARSE and (b) MMP16, normalized gene expression depicted by the heatmap from SCTransform normalized read counts. Individual sequencing libraries across four studies were harmonized after QC and batch correction with reciprocal PCA (rPCA). Clusters were annotated with level 1 cell type labels using transfer learning with cell labels from the Tabula Sapiens vasculature subset. Level 2 cell type label for endothelial-mesenchymal transition (EndoMT) endothelial cells expressing ARSE and MMP16 are also highlighted. (c-d) Scatter plots showing the normalized expression level of (c) ARSE and (d) MMP16, across the level 1 cell types. EC: Endothelial cells; SMC: Smooth muscle cells; T/NK: T cells and Natural Killer cells; pDC: plasmacytoid dendritic cells.
Extended Data Fig. 5 Silencing MMP16 expression has no effect on osteogenic phenotype switching in human coronary artery vascular smooth muscle cells.
a) Treatment of human coronary artery vascular smooth muscle cells (n = 6 biologically independent samples in each group) with osteogenic media decreased MMP16 mRNA expression by ~74% (left panel). Treatment of cells grown in osteogenic media with siMMP16 (resulting in >90% knockdown of MMP16 mRNA) had no effect on RUNX2 (middle panel) or CNN1 (right panel) mRNA levels. Statistical comparisons were made using a two-tailed one-way ANOVA with Sidak’s post-hoc comparison testing. The mean ± SEM is depicted in plots. b) Treatment of human coronary artery vascular smooth muscle cells grown in osteogenic media with siMMP16 had no effect on calcification, as evidenced by Alizarin Red S staining.
Extended Data Fig. 6 Silencing ARSE expression increases contractile gene expression in human aortic vascular smooth muscle cells.
Silencing ARSE in cells (n = 12 biologically independent samples in each group) grown in normal media increased a) ACTA2 and b) TAGLN (SM22α) mRNA levels by ~ 56% and 35%, respectively. Statistical comparisons were made using a two-tailed Student t test. The mean is depicted in plots, with the error bars representing the standard error of the mean.
Extended Data Fig. 7 Human aortic vascular smooth muscle cell calcification, bone and contractile marker expression, and contractility are affected by changes in ARSE expression.
a) Treatment of human aortic vascular smooth muscle cells (n = 12 biologically independent samples in each group) with osteogenic media increased ARSE mRNA expression > 2-fold. b) Treatment of cells grown in osteogenic media with siARSE (resulting in >90% knockdown of ARSE mRNA) decreased RUNX2 (left panel), and BGLAP (middle panel) mRNA levels by ~20% and ~43% respectively, and increased CNN1 mRNA levels by > 150% (right panel). Silencing ARSE in cells grown in normal media increased CNN1 mRNA levels by > 2.5-fold. c) Treatment of cells grown in osteogenic media with siARSE reduced calcification, as evidenced by decreased Alizarin Red S staining (n = 5 biologically independent samples in each group). d) Reduced ARSE expression in cells grown in collagen discs (left panel) resulted in a >3-fold increase in contraction (right panel, n = 6 biologically independent samples in each group). e) Protein expression of ARSE, RUNX2 and CNN1 were confirmed by Western blot using antibodies directed against ARSE, RUNX2, CNN1 and GAPDH (for a loading control). Adenoviral expression of the 70-kDa isoform of ARSE in human aortic vascular smooth muscle cells was associated with a >15-fold increase in RUNX2 protein levels and an approximately 34% decrease in CNN1 protein levels, when cells were harvested 5 days after viral transduction (n = 3 biologically independent samples in each group). f) As shown by Alizarin Red staining, increased ARSE expression resulted in augmented calcification in human aortic vascular smooth muscle cells (n = 3 biologically independent samples in each group). g) Increased ARSE expression also caused a >70% decrease (right panel, n = 6 biologically independent samples in each group) in contraction of human aortic vascular smooth muscle cells grown in collagen discs (left panel). Statistical comparisons were made using either a two-tailed one-way ANOVA with Sidak’s post-hoc comparison testing or a two-tailed Student t test. The mean is depicted in plots, with the error bars representing the standard error of the mean.
Extended Data Fig. 8 ARSE expression and calcification in normal and ischemic human coronary arteries.
a) Cross sections of human coronary arteries from control subjects and patients with ischemic coronary artery disease (n = 3 individuals in each group with 2 sections stained for each individual) were stained for ARSE (red), α-smooth muscle actin (green) and DNA (blue, DAPI). Immunofluorescence analysis shows a higher expression of ARSE in diseased arteries. Alizarin red staining for calcification was high in the coronary arteries of diseased patients with no appreciable Alizarin stain observed in the control group. Scale bars, 200 µm for each immunofluorescence image; 500 µm for each Alizarin red staining image. Statistical comparisons were made using a two-tailed Student’s t test. The mean is depicted in plots, with the error bars representing the standard error of the mean. b) Cross sections of human coronary arteries (n = 1 each for control and ischemic patient) were stained for ARSE (red), RUNX2 (green, VSMC calcification marker) and DNA (blue, DAPI). Immunofluorescence analysis shows a higher expression of ARSE in calcified diseased arteries that colocalized with increased RUNX2 expression. Scale bar 500 µm.
Extended Data Fig. 9 Luciferase reporter assay to analyze the functional impact of SNP rs5982944 (A/G).
The rs5982944-A allele and rs5982944-G allele firefly luciferase constructs were co-transfected with renilla luciferase plasmid into human coronary smooth muscle cells (HCSMCs), human aortic smooth muscle cells (HASMCs) and HEK-293 cells (n = 6 biologically independent samples for HCSMCs and HASMCs and n = 5 biologically independent samples for HEK-293). Firefly luciferase activity and renilla luciferase activity (internal control reporter vector) were measured sequentially in cell lysates. Firefly luciferase activity in cell lysates transfected with either the rs5982944-A construct or the rs5982944-G construct was normalized to renilla luciferase activity. The mean is depicted in plots, with the error bars representing the standard error of the mean. Statistical comparisons were made using the two-tailed unpaired t-test.
Extended Data Fig. 10 Model of ARSE-induced phenotype switch from contractile to osteogenic vascular smooth muscle cells.
Atherosclerotic vascular calcification is characterized by the phenotype switch of vascular smooth muscle cells (VSMCs) from a contractile phenotype to a proliferative, osteogenic phenotype. The osteogenic phenotype of VSMCs is characterized by decreased expression of contractile proteins such as calponin (CNN1), but increased expression of Runt-related transcription factor 2 (RUNX2), a master regulator of the phenotype switch, in addition to other markers of calcification such as bone gamma-carboxyglutamate protein (BGLAP) and alkaline phosphatase (ALPL). We identified ARSE as a major regulator of the phenotype switch.
Supplementary information
Supplementary information
Supplementary Figs. 1–10, Supplementary Methods and Supplementary References
Source data
Source Data Fig. 2
Unprocessed western blots
Source Data Fig. 3
Unprocessed western blots
Source Data Extended Data Fig./Table 7
Unprocessed western blots
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
de Vries, P.S., Conomos, M.P., Singh, K. et al. Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification. Nat Cardiovasc Res 2, 1159–1172 (2023). https://doi.org/10.1038/s44161-023-00375-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s44161-023-00375-y
This article is cited by
-
Unveiling novel genetic insights into arterial calcification
Nature Cardiovascular Research (2023)
