Abstract
Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.
Key points
-
Cardiovascular diseases are complex states, with the effect of the environment usually being far greater than that of the genetic status of an individual; therefore, the understanding of cardiovascular disease requires investigation of many biological levels.
-
Omics techniques generate very large, complex and non-linear datasets, which mandate a systems biology approach, that is, the understanding of a biological process through examining the interactions between heterogeneous components.
-
Current systems biology approaches applying network theories or machine learning to single-platform omics data have helped to make some progress in understanding cardiovascular disease but caveats remain.
-
Integrated multiomics approaches explain the interactions between omics dimensions and are likely to require new ontological approaches to describe their findings.
-
The lure of obtaining large datasets should not replace thoughtful, well-designed experiments; investigators must understand the technical and biological limitations of omics approaches alongside their strengths.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 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
References
Yates, A. D. et al. Ensembl 2020. Nucleic Acids Res. 48, D682–D688 (2020).
Gagniuc, P. & Ionescu-Tirgoviste, C. Gene promoters show chromosome-specificity and reveal chromosome territories in humans. BMC Genomics 14, 278 (2013).
Mercer, T. R., Dinger, M. E. & Mattick, J. S. Long non-coding RNAs: insights into functions. Nat. Rev. Genet. 10, 155–159 (2009).
Barwari, T., Joshi, A. & Mayr, M. MicroRNAs in cardiovascular disease. J. Am. Coll. Cardiol. 68, 2577–2584 (2016).
Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (2014).
Shang, L. L. et al. Human heart failure is associated with abnormal C-terminal splicing variants in the cardiac sodium channel. Circ. Res. 101, 1146–1154 (2007).
Rosas, P. C. et al. Phosphorylation of cardiac myosin-binding protein-C Is a critical mediator of diastolic function. Circ. Hear. Fail. 8, 582–594 (2015).
Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).
Hopkins, A. L. & Groom, C. R. The druggable genome. Nat. Rev. Drug Discov. 1, 727–730 (2002).
Russ, A. P. & Lampel, S. The druggable genome: an update. Drug Discov. Today 10, 1607–1610 (2005).
Griffith, M. et al. DGIdb: mining the druggable genome. Nat. Methods 10, 1209–1210 (2013).
Kohane, I. S. Using electronic health records to drive discovery in disease genomics. Nat. Rev. Genet. 12, 417–428 (2011).
Denny, J. C. Chapter 13: mining electronic health records in the genomics Era. PLoS Comput. Biol. 8, e1002823 (2012).
Jensen, P. B., Jensen, L. J. & Brunak, S. Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13, 395–405 (2012).
Morley, K. I. et al. Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation. PLoS ONE 9, e110900 (2014).
Berger, B., Peng, J. & Singh, M. Computational solutions for omics data. Nat. Rev. Genet. 14, 333–346 (2013).
Huang, S.-S. C. & Fraenkel, E. Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Sci. Signal. 2, ra40 (2009).
Auffray, C. et al. From genomic medicine to precision medicine: highlights of 2015. Genome Med. 8, 12 (2016).
Kathiresan, S. et al. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat. Genet. 41, 334–341 (2009).
Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).
Reilly, M. P. et al. Identification of ADAMTS7 as a novel locus for coronary atherosclerosis and association of ABO with myocardial infarction in the presence of coronary atherosclerosis: two genome-wide association studies. Lancet 377, 383–392 (2011).
Vargas, J. D. et al. Common genetic variants and subclinical atherosclerosis in the multi-ethnic study of atherosclerosis. Circulation 128, 230–236 (2013).
Ellinor, P. T. et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat. Genet. 44, 670–675 (2012).
Kao, W. H. L. et al. Genetic variations in nitric oxide synthase 1 adaptor protein are associated with sudden cardiac death in US white community-based populations. Circulation 119, 940–951 (2009).
Villard, E. et al. A genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy. Eur. Heart J. 32, 1065–1076 (2011).
Meder, B. et al. A genome-wide association study identifies 6p21 as novel risk locus for dilated cardiomyopathy. Eur. Heart J. 35, 1069–1077 (2014).
Piñero, J. et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database 2015, bav028 (2015).
Liu, D. J. et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat. Genet. 49, 1758–1766 (2017).
Lu, X. et al. Exome chip meta-analysis identifies novel loci and East Asian–specific coding variants that contribute to lipid levels and coronary artery disease. Nat. Genet. 49, 1722–1730 (2017).
Zhao, W. et al. Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat. Genet. 49, 1450–1457 (2017).
Larson, M. G. et al. Framingham Heart Study 100K project: genome-wide associations for cardiovascular disease outcomes. BMC Med. Genet. 8, S5 (2007).
Shen, G.-Q. et al. Four SNPs on chromosome 9p21 in a South Korean Population Implicate a Genetic Locus that confers high cross-race risk for development of coronary artery disease. Arterioscler. Thromb. Vasc. Biol. 28, 360–365 (2008).
Shen, G.-Q. et al. Association between four SNPs on chromosome 9p21 and myocardial infarction is replicated in an Italian population. J. Hum. Genet. 53, 144–150 (2008).
Schunkert, H. et al. Repeated replication and a prospective meta-analysis of the association between chromosome 9p21.3 and coronary artery disease. Circulation 117, 1675–1684 (2008).
Anderson, J. L. et al. Genetic variation at the 9p21 locus predicts angiographic coronary artery disease prevalence but not extent and has clinical utility. Am. Heart J. 156, 1155–1162.e2 (2008).
Samani, N. J. et al. Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 357, 443–453 (2007).
Helgadottir, A. et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 316, 1491–1493 (2007).
Patel, R. S. et al. Genetic variants at chromosome 9p21 and risk of first versus subsequent coronary heart disease events: a systematic review and meta-analysis. J. Am. Coll. Cardiol. 63, 2234–2245 (2014).
Patel, R. S. et al. Association of chromosome 9p21 with subsequent coronary heart disease events: A GENIUS-CHD study of individual participant data. Circ. Genomic Precis. Med. 12, 161–172 (2019).
Holdt, L. M. et al. Circular non-coding RNA ANRIL modulates ribosomal RNA maturation and atherosclerosis in humans. Nat. Commun. 7, 12429 (2016).
Holdt, L. M. et al. ANRIL expression is associated with atherosclerosis risk at chromosome 9p21. Arterioscler. Thromb. Vasc. Biol. 30, 620–627 (2010).
Lo Sardo, V. et al. Unveiling the role of the most impactful cardiovascular risk locus through haplotype editing. Cell 175, 1796–1810.e20 (2018).
Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults. J. Am. Coll. Cardiol. 72, 1883–1893 (2018).
Abraham, G. et al. Genomic prediction of coronary heart disease. Eur. Heart J. 37, 3267–3278 (2016).
Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).
Cornelis, M. C. et al. The gene, environment association studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions. Genet. Epidemiol. 34, 364–372 (2010).
Murcray, C. E., Lewinger, J. P. & Gauderman, W. J. Gene-environment interaction in genome-wide association studies. Am. J. Epidemiol. 169, 219–226 (2009).
Mars, N. et al. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat. Med. 26, 549–557 (2020).
Sohail, M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife 8, e39702 (2019).
Berg, J. J. et al. Reduced signal for polygenic adaptation of height in UK biobank. eLife 8, e39725 (2019).
Li, Y. et al. Statistical and functional studies identify epistasis of cardiovascular risk genomic variants from genome-wide association studies. J. Am. Heart Assoc. 9, e014146 (2020).
Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N. & Davey Smith, G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27, 1133–1163 (2008).
Ference, B. A. et al. Mendelian randomization study of ACLY and cardiovascular disease. N. Engl. J. Med. 380, 1033–1042 (2019).
Ray, K. K. et al. Safety and efficacy of bempedoic acid to reduce LDL cholesterol. N. Engl. J. Med. 380, 1022–1032 (2019).
Sanderson, E., Davey Smith, G., Windmeijer, F. & Bowden, J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int. J. Epidemiol. 48, 713–727 (2019).
Zuber, V., Colijn, J. M., Klaver, C. & Burgess, S. Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization. Nat. Commun. 11, 29 (2020).
Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).
Minikel, E. V. et al. Evaluating drug targets through human loss-of-function genetic variation. Nature 581, 459–464 (2020).
Timpson, N. J. et al. C-reactive protein and its role in metabolic syndrome: Mendelian randomisation study. Lancet 366, 1954–1959 (2005).
Richardson, T. G., Harrison, S., Hemani, G. & Smith, G. D. An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome. eLife 8, e43657 (2019).
Tillmann, T. et al. Education and coronary heart disease: Mendelian randomisation study. BMJ 358, j3542 (2017).
Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
Liu, X., Li, Y. I. & Pritchard, J. K. Trans effects on gene expression can drive omnigenic inheritance. Cell 177, 1022–1034.e6 (2019).
Wray, N. R., Wijmenga, C., Sullivan, P. F., Yang, J. & Visscher, P. M. Common disease is more complex than implied by the core gene omnigenic model. Cell 173, 1573–1580 (2018).
Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).
Huan, T. et al. A systems biology framework identifies molecular underpinnings of coronary heart disease. Arterioscler. Thromb. Vasc. Biol. 33, 1427–1434 (2013).
Preuss, M. et al. Design of the Coronary Artery Disease Genome-wide Replication and Meta-analysis (CARDIoGRAM) study. Circ. Cardiovasc. Genet. 3, 475–483 (2010).
Holm, H. et al. Several common variants modulate heart rate, PR interval and QRS duration. Nat. Genet. 42, 117 (2010).
Chambers, J. C. et al. Genetic variation in SCN10A influences cardiac conduction. Nat. Genet. 42, 149–152 (2010).
Pfeufer, A. et al. Genome-wide association study of PR interval. Nat. Genet. 42, 153 (2010).
Noseworthy, P. A. & Newton-Cheh, C. Genetic determinants of sudden cardiac death. Circulation 118, 1854–1863 (2008).
Lubitz, S. A. et al. Association between familial atrial fibrillation and risk of new-onset atrial fibrillation. JAMA 304, 2263 (2010).
Lin, H. et al. Gene expression and genetic variation in human atria. Heart Rhythm. 11, 266–271 (2014).
Kertai, M. D. et al. Genome-wide association study of new-onset atrial fibrillation after coronary artery bypass grafting surgery. Am. Heart J. 170, 580 (2015).
Gupta, R. M. & Musunuru, K. Mapping novel pathways in cardiovascular disease using eQTL data: the past, present, and future of gene expression analysis. Front. Genet. 3, 232 (2013).
Cookson, W., Liang, L., Abecasis, G., Moffatt, M. & Lathrop, M. Mapping complex disease traits with global gene expression. Nat. Rev. Genet. 10, 184–194 (2009).
Brem, R. B. et al. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002).
Rockman, M. V. & Kruglyak, L. Genetics of global gene expression. Nat. Rev. Genet. 7, 862–872 (2006).
Michaelson, J. J., Loguercio, S. & Beyer, A. Detection and interpretation of expression quantitative trait loci (eQTL). Methods 48, 265–276 (2009).
Lappalainen, T., Scott, A. J., Brandt, M. & Hall, I. M. Genomic analysis in the age of human genome sequencing. Cell 177, 70–84 (2019).
Stranger, B. E. et al. Population genomics of human gene expression. Nat. Genet. 39, 1217–1224 (2007).
Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).
Ardlie, K. G. et al. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).
Linsel-Nitschke, P. et al. Genetic variation at chromosome 1p13.3 affects sortilin mRNA expression, cellular LDL-uptake and serum LDL levels which translates to the risk of coronary artery disease. Atherosclerosis 208, 183–189 (2010).
Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).
Wu, P. Y. et al. Cardiovascular transcriptomics and epigenomics using next-generation sequencing challenges, progress, and opportunities. Circ. Cardiovasc. Genet. 7, 701–710 (2014).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Mohammadi, P. et al. Genetic regulatory variation in populations informs transcriptome analysis in rare disease. Science 366, 351–356 (2019).
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Miller, C. L. et al. Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci. Nat. Commun. 7, 12092 (2016).
Eichler, E. E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450 (2010).
Talukdar, H. A. et al. Cross-tissue regulatory gene networks in coronary artery disease. Cell Syst. 2, 196–208 (2016).
Frades, I. et al. Systems pharmacology identifies an arterial wall regulatory gene network mediating coronary artery disease side effects of antiretroviral therapy. Circ. Genomic Precis. Med. 12, 262–272 (2019).
Franzén, O. et al. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science 353, 827–830 (2016).
Vilne, B. et al. Network analysis reveals a causal role of mitochondrial gene activity in atherosclerotic lesion formation. Atherosclerosis 267, 39–48 (2017).
Walter, W. et al. Deciphering the dynamic transcriptional and post-transcriptional networks of macrophages in the healthy heart and after myocardial injury. Cell Rep. 23, 622–636 (2018).
van Heesch, S. et al. The translational landscape of the human heart. Cell 178, 242–260.e29 (2019).
Wang, S. et al. The endothelial-specific microRNA miR-126 governs vascular integrity and angiogenesis. Dev. Cell 15, 261–271 (2008).
Zampetaki, A. et al. Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circ. Res. 107, 810–817 (2010).
Willeit, P. et al. Circulating microRNAs as novel biomarkers for platelet activation. Circ. Res. 112, 595–600 (2013).
Kaudewitz, D. et al. Association of microRNAs and YRNAs with platelet function. Circ. Res. 118, 420–432 (2016).
Savitski, M. M. et al. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J. Proteome Res. 12, 3586–3598 (2013).
Werner, T. et al. Ion Coalescence of neutron encoded TMT 10-plex reporter ions. Anal. Chem. 86, 3594–3601 (2014).
Mann, M. Functional and quantitative proteomics using SILAC. Nat. Rev. Mol. Cell Biol. 7, 952–958 (2006).
Ong, S.-E. & Mann, M. A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat. Protoc. 1, 2650–2660 (2007).
Gstaiger, M. & Aebersold, R. Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat. Rev. Genet. 10, 617–627 (2009).
Witze, E. S., Old, W. M., Resing, K. A. & Ahn, N. G. Mapping protein post-translational modifications with mass spectrometry. Nat. Methods 4, 798–806 (2007).
Kusebauch, U. et al. Human SRMAtlas: a resource of targeted assays to quantify the complete human proteome. Cell 166, 766–778 (2016).
Tu, C. et al. Depletion of abundant plasma proteins and limitations of plasma proteomics. J. Proteome Res. 9, 4982–4991 (2010).
Schubert, O. T., Röst, H. L., Collins, B. C., Rosenberger, G. & Aebersold, R. Quantitative proteomics: challenges and opportunities in basic and applied research. Nat. Protoc. 12, 1289–1294 (2017).
Doerr, A. DIA mass spectrometry. Nat. Methods 12, 35 (2015).
Bom, M. J. et al. Predictive value of targeted proteomics for coronary plaque morphology in patients with suspected coronary artery disease. EBioMedicine 39, 109–117 (2019).
Langley, S. R. et al. Extracellular matrix proteomics identifies molecular signature of symptomatic carotid plaques. J. Clin. Invest. 127, 1546–1560 (2017).
Willeit, K. et al. Association between vascular cell adhesion molecule 1 and atrial fibrillation. JAMA Cardiol. 2, 516 (2017).
Smith, J. G. & Gerszten, R. E. Emerging affinity-based proteomic technologies for large-scale plasma profiling in cardiovascular disease. Circulation 135, 1651–1664 (2017).
Rohloff, J. C. et al. Nucleic acid ligands with protein-like side chains: Modified aptamers and their use as diagnostic and therapeutic agents. Mol. Ther. Nucleic Acids 3, e201 (2014).
Ngo, D. et al. Aptamer-based proteomic profiling reveals novel candidate biomarkers and pathways in cardiovascular disease. Circulation 134, 270–285 (2016).
Benson, M. D. et al. Application of large-scale aptamer-based proteomic profiling to planned myocardial infarctions. Circulation 137, 1270–1277 (2017).
Jacquet, S. et al. Identification of cardiac myosin-binding protein C as a candidate biomarker of myocardial infarction by proteomics analysis. Mol. Cell. Proteom. 8, 2687–2699 (2009).
Marjot, J. et al. Quantifying the release of biomarkers of myocardial necrosis from cardiac myocytes and intact myocardium. Clin. Chem. 63, 990–996 (2017).
Kaier, T. E. et al. Direct comparison of cardiac myosin-binding protein C with cardiac troponins for the early diagnosis of acute myocardial infarction. Circulation 136, 1495–1508 (2017).
Ganz, P. et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA 315, 2532 (2016).
Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).
Williams, S. A. et al. Plasma protein patterns as comprehensive indicators of health. Nat. Med. 25, 1851–1857 (2019).
Mosley, J. et al. Probing the virtual proteome to identify novel disease biomarkers. Circulation. 138, 2469–2481 (2018).
Christiansson, L. et al. The use of multiplex platforms for absolute and relative protein quantification of clinical material. EuPA Open Proteom. 3, 37–47 (2014).
Wei, W.-Q. et al. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLoS ONE 12, e0175508 (2017).
Wadhera, R. K. et al. Temporal trends in unstable angina diagnosis codes for outpatient percutaneous coronary interventions. JAMA Intern. Med. 179, 259–261 (2019).
Lehallier, B. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat. Med. 25, 1843–1850 (2019).
Rual, J.-F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173–1178 (2005).
Vidal, M., Cusick, M. E. & Barabasi, A. L. Interactome networks and human disease. Cell 144, 986–998 (2011).
Jensen, M. K. et al. Protein interaction-based genome-wide analysis of incident coronary heart disease. Circ. Cardiovasc. Genet. 4, 549–556 (2011).
O’Reilly, F. J. & Rappsilber, J. Cross-linking mass spectrometry: methods and applications in structural, molecular and systems biology. Nat. Struct. Mol. Biol. 25, 1000–1008 (2018).
Liao, Y. et al. The cardiomyocyte RNA-binding proteome: links to intermediary metabolism and heart disease. Cell Rep. 16, 1456–1469 (2016).
Shah, S. H. & Newgard, C. B. Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease. Circ. Cardiovasc. Genet. 8, 410–419 (2015).
Hoefer, I. E. et al. Novel methodologies for biomarker discovery in atherosclerosis. Eur. Heart J. 36, 2635–2642 (2015).
Mundra, P. A. et al. Large-scale plasma lipidomic profiling identifies lipids that predict cardiovascular events in secondary prevention. JCI Insight 3, e121326 (2018).
Huynh, K. et al. High-throughput plasma lipidomics: detailed mapping of the associations with cardiometabolic risk factors. Cell Chem. Biol. 26, 71–84.e4 (2019).
Karatasakis, A. et al. Effect of PCSK9 inhibitors on clinical outcomes in patients with hypercholesterolemia: a meta-analysis of 35 randomized controlled trials. J. Am. Heart Assoc. 6, e006910 (2017).
Soininen, P. et al. High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst 134, 1781 (2009).
Sliz, E. et al. Metabolomic consequences of genetic inhibition of PCSK9 compared with statin treatment. Circulation 138, 2499–2512 (2018).
Stegemann, C. et al. Lipidomics profiling and risk of cardiovascular disease in the prospective population-based bruneck study. Circulation 129, 1821–1831 (2014).
Pechlaner, R. et al. Very-low-density lipoprotein–associated apolipoproteins predict cardiovascular events and are lowered by inhibition of APOC-III. J. Am. Coll. Cardiol. 69, 789–800 (2017).
Gaudet, D. et al. Antisense inhibition of apolipoprotein C-III in patients with hypertriglyceridemia. N. Engl. J. Med. 373, 438–447 (2015).
Würtz, P. et al. Metabolite profiling and cardiovascular event risk. Circulation 131, 774–785 (2015).
Nordestgaard, B. G. & Varbo, A. Triglycerides and cardiovascular disease. Lancet 384, 626–635 (2014).
Ference, B. A. et al. Association of triglyceride-lowering LPL variants and LDL-C–lowering LDLR variants with risk of coronary heart disease. JAMA 321, 364 (2019).
Richardson, T. G. et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis. PLoS Med. 17, e1003062 (2020).
Ng, T. W. K. et al. Association of Plasma ceramides and sphingomyelin with VLDL apoB-100 fractional catabolic rate before and after rosuvastatin treatment. J. Clin. Endocrinol. Metab. 100, 2497–2501 (2015).
Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).
Org, E. et al. Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort. Genome Biol. 18, 70 (2017).
Tang, W. H. W. & Hazen, S. L. The gut microbiome and its role in cardiovascular diseases. Circulation 135, 1008–1010 (2017).
Jie, Z. et al. The gut microbiome in atherosclerotic cardiovascular disease. Nat. Commun. 8, 845 (2017).
Walter, J., Armet, A. M., Finlay, B. B. & Shanahan, F. Establishing or exaggerating causality for the gut microbiome: lessons from human microbiota-associated rodents. Cell 180, 221–232 (2020).
Mayr, M. et al. Proteomic and metabolomic analyses of atherosclerotic vessels from apolipoprotein E-deficient mice reveal alterations in inflammation, oxidative stress, and energy metabolism. Arterioscler. Thromb. Vasc. Biol. 25, 2135–2142 (2005).
Klipfell, E. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).
Tang, W. H. W. et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 368, 1575–1584 (2013).
Haghikia, A. et al. Gut microbiota-dependent trimethylamine N-oxide predicts risk of cardiovascular events in patients with stroke and is related to proinflammatory monocytes. Arterioscler. Thromb. Vasc. Biol. 38, 2225–2235 (2018).
Zhu, W. et al. Gut microbial metabolite TMAO enhances platelet hyperreactivity and thrombosis risk. Cell 165, 111–124 (2016).
Roberts, A. B. et al. Development of a gut microbe–targeted nonlethal therapeutic to inhibit thrombosis potential. Nat. Med. 24, 1407–1417 (2018).
van Mens, T. E., Büller, H. R. & Nieuwdorp, M. Targeted inhibition of gut microbiota proteins involved in TMAO production to reduce platelet aggregation and arterial thrombosis: a blueprint for drugging the microbiota in the treatment of cardiometabolic disease? J. Thromb. Haemost. 17, 3–5 (2019).
Manor, O. et al. A multi-omic association study of trimethylamine N-oxide. Cell Rep. 24, 935–946 (2018).
Heianza, Y. et al. Long-term changes in gut microbial metabolite trimethylamine N-oxide and coronary heart disease risk. J. Am. Coll. Cardiol. 75, 763–772 (2020).
Gencer, B. et al. Gut microbiota-dependent trimethylamine N-oxide and cardiovascular outcomes in patients with prior myocardial infarction: a nested case control study from the PEGASUS-TIMI 54 Trial. J. Am. Heart Assoc. 9, e015331 (2020).
Vieira-Silva, S. et al. Statin therapy is associated with lower prevalence of gut microbiota dysbiosis. Nature 581, 310–315 (2020).
Chan, S. Y. & Loscalzo, J. The emerging paradigm of network medicine in the study of human disease. Circ. Res. 111, 359–374 (2012).
Pasea, L. et al. Personalising the decision for prolonged dual antiplatelet therapy: development, validation and potential impact of prognostic models for cardiovascular events and bleeding in myocardial infarction survivors. Eur. Heart J. 38, 1048–1055 (2017).
Needham, C. J., Bradford, J. R., Bulpitt, A. J. & Westhead, D. R. A primer on learning in bayesian networks for computational biology. PLoS Comput. Biol. 3, e129 (2007).
Shilaskar, S. & Ghatol, A. Feature selection for medical diagnosis: evaluation for cardiovascular diseases. Expert Syst. Appl. 40, 4146–4153 (2013).
Sanz, J. A. et al. Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system. Appl. Soft Comput. 20, 103–111 (2014).
Libbrecht, M. W. & Noble, W. S. Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321–332 (2015).
Tarca, A. L. et al. Machine learning and its applications to biology. PLoS Comput. Biol. 3, e116 (2007).
Liu, Y. et al. Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome. Sci. Rep. 6, 34540 (2016).
Shah, S. J. et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 131, 269–279 (2015).
Sengupta, P. P. et al. Cognitive machine-learning algorithm for cardiac imaging. Circ. Cardiovasc. Imaging 9, e004330 (2016).
Motwani, M. et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur. Heart J. 38, 500–507 (2016).
Raghunath, S. et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat. Med. 26, 886–891 (2020).
Dey, D. et al. Artificial intelligence in cardiovascular imaging: jacc state-of-the-art review. J. Am. Coll. Cardiol. 73, 1317–1335 (2019).
McNally, E. M. et al. Genetic mutations and mechanisms in dilated cardiomyopathy. J. Clin. Invest. 123, 19–26 (2013).
Hershberger, R. E., Hedges, D. J. & Morales, A. Dilated cardiomyopathy: the complexity of a diverse genetic architecture. Nat. Rev. Cardiol. 10, 531–547 (2013).
Stark, K. et al. Genetic association study identifies HSPB7 as a risk gene for idiopathic dilated cardiomyopathy. PLoS Genet. 6, e1001167 (2010).
Norton, N. et al. Exome sequencing and genome-wide linkage analysis in 17 families illustrate the complex contribution of TTN truncating variants to dilated cardiomyopathy. Circ. Cardiovasc. Genet. 6, 144–153 (2013).
Camargo, A. & Azuaje, F. Identification of dilated cardiomyopathy signature genes through gene expression and network data integration. Genomics 92, 404–413 (2008).
Liu, Y. et al. RNA-seq identifies novel myocardial gene expression signatures of heart failure. Genomics 105, 83–89 (2015).
Lau, E. et al. Integrated omics dissection of proteome dynamics during cardiac remodeling. Nat. Commun. 9, 120 (2018).
Isserlin, R. et al. Systems analysis reveals down-regulation of a network of pro-survival miRNAs drives the apoptotic response in dilated cardiomyopathy. Mol. Biosyst. 11, 239–251 (2015).
Inouye, M. et al. An immune response network associated with blood lipid levels. PLoS Genet. 6, e1001113 (2010).
Inouye, M. et al. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol. Syst. Biol. 6, 441 (2010).
Bartel, J. et al. The human blood metabolome-transcriptome interface. PLoS Genet. 11, e1005274 (2015).
Tabassum, R. et al. Genetic architecture of human plasma lipidome and its link to cardiovascular disease. Nat. Commun. 10, 4329 (2019).
Inouye, M. et al. Novel loci for metabolic networks and multi-tissue expression studies reveal genes for atherosclerosis. PLoS Genet. 8, e1002907 (2012).
Gallois, A. et al. A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context. Nat. Commun. 10, 4788 (2019).
Voros, S. et al. Precision phenotyping, panomics, and system-level bioinformatics to delineate complex biologies of atherosclerosis: Rationale and design of the ‘Genetic Loci and the Burden of Atherosclerotic Lesions’ study. J. Cardiovasc. Comput. Tomogr. 8, 442–451 (2014).
Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).
Choobdar, S. et al. Assessment of network module identification across complex diseases. Nat. Methods 16, 843–852 (2019).
Bennett, L., Kittas, A., Muirhead, G., Papageorgiou, L. G. & Tsoka, S. Detection of composite communities in multiplex biological networks. Sci. Rep. 5, 10345 (2015).
Pinto, A. R. et al. Revisiting cardiac cellular composition. Circ. Res. 118, 400–409 (2016).
Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).
Tang, F. et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Wang, D. & Bodovitz, S. Single cell analysis: the new frontier in ‘omics’. Trends Biotechnol. 28, 281–290 (2010).
Thul, P. J. et al. A subcellular map of the human proteome. Science 356, eaal3321 (2017).
Spitzer, M. H. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).
Sun, Z. et al. A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies. Nat. Commun. 10, 1649 (2019).
Liu, Z. et al. Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte. Nature 551, 100–104 (2017).
Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).
Tucker, N. R. et al. Transcriptional and cellular diversity of the human heart. Circulation 142, 466–482 (2020).
Cascante, M. et al. Metabolic control analysis in drug discovery and disease. Nat. Biotechnol. 20, 243–249 (2002).
Duarte, N. C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl Acad. Sci. USA 104, 1777–1782 (2007).
Karlstädt, A. et al. CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism. BMC Syst. Biol. 6, 114 (2012).
Edwards, L. M. et al. Genome-scale methods converge on key mitochondrial genes for the survival of human cardiomyocytes in hypoxia. Circ. Cardiovasc. Genet. 7, 407–415 (2014).
J. S. Mill A System of Logic Bk III, Ch. 6, §1
Califf, R. M. Future of personalized cardiovascular medicine: JACC state-of-the-art review. J. Am. Coll. Cardiol. 72, 3301–3309 (2018).
Smith, G. D. Epidemiology, epigenetics and the ‘Gloomy Prospect’: embracing randomness in population health research and practice. Int. J. Epidemiol. 40, 537–562 (2011).
Trachana, K. et al. Taking systems medicine to heart. Circ. Res. 122, 1276–1289 (2018).
Leopold, J. A. & Loscalzo, J. Emerging role of precision medicine in cardiovascular disease. Circ. Res. 122, 1302–1315 (2018).
Lempiäinen, H. et al. Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets. Sci. Rep. 8, 3434 (2018).
Chan, S. Y. & Loscalzo, J. The emerging paradigm of network medicine in the study of human disease. Circ. Res. 111, 359–374 (2012).
Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).
Ma’ayan, A. Introduction to network analysis in systems biology. Sci. Signal. 4, tr5 (2011).
Schadt, E. E. & Lum, P. Y. Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J. Lipid Res. 47, 2601–2613 (2006).
Cheng, L. et al. Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks. IEEE Trans. Neural Netw. 22, 714–726 (2011).
McGeachie, M. J. et al. CGBayesNets: conditional gaussian bayesian network learning and inference with mixed discrete and continuous data. PLoS Comput. Biol. 10, e1003676 (2014).
Sarajlic´, A., Janjic´, V., Stojkovic´, N., Radak, D. J. & Pržulj, N. Network topology reveals key cardiovascular disease genes. PLoS ONE 8, e71537 (2013).
Dewey, F. E. et al. Gene coexpression network topology of cardiac development, hypertrophy, and failure. Circ. Cardiovasc. Genet. 4, 26–35 (2011).
Ravasz, E. et al. Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002).
de la Fuente, A. From ‘differential expression’ to ‘differential networking’ - identification of dysfunctional regulatory networks in diseases. Trends Genet. 26, 326–333 (2010).
Acknowledgements
A.J. is a British Heart Foundation (BHF) Clinical Research Training Fellow (FS/16/32/32184). M.M. is a BHF Chair Holder (CH/16/3/32406) and receiving BHF programme grant support (RG/16/14/32397). Research by M.M. was made possible through support from the BIRAX Regenerative Medicine Initiative and funding from the EU Horizon 2020 Research and Innovation Programme under Marie Skłodowska-Curie grant agreement No 813716 (TRAIN-HEART), the Leducq Foundation (13CVD02 and 18CVD02), the Excellence Initiative VASCage (Centre for Promoting Vascular Health in the Ageing Community, project number 868624) of the Austrian Research Promotion Agency FFG (COMET program – Competence Centers for Excellent Technologies) funded by the Austrian Ministry for Transport, Innovation and Technology, the Austrian Ministry for Digital and Economic Affairs and the federal states Tyrol (via Standortagentur), Salzburg, and Vienna (via Vienna Business Agency), and the National Institute of Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS (National Health Service) Foundation Trust and King’s College London in partnership with King’s College Hospital. We thank Raimund Pechlaner (Medical University of Innsbruck, Austria) for providing Fig. 4.
Author information
Authors and Affiliations
Contributions
All authors researched data for the article, discussed its content, wrote the manuscript, and reviewed and edited it before submission.
Corresponding author
Ethics declarations
Competing interests
M.M. is a named inventor on patents for cardiovascular biomarkers. The other authors declare no competing interests.
Additional information
Peer review information
Nature Reviews Cardiology thanks M. Ala-Korpela and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Related links
CALIBER research: https://caliberresearch.org/portal
Glossary
- Enrichment analysis
-
A set of bioinformatics and statistical techniques that identify classes of molecules (such as genes or proteins) that are over-represented in a large dataset and might have an association with a functional term, a biological pathway or disease phenotypes.
- Expression quantitative trait loci
-
(eQTL). Places on the genome in which polymorphisms explain a significant proportion of the variation in mRNA expression levels.
- Mass spectrometry
-
A category of analytical tool that is used to measure the mass-to-charge ratio of one or more molecules present in a sample. Typically, mass spectrometry can be used to identify and quantify unknown compounds and to determine the structure and chemical properties of molecules.
- Data-independent acquisition
-
Mode of data collection in mass spectrometry, in which all precursor ions within defined mass to charge windows are fragmented sequentially, rather than the selection and fragmentation of the most abundant precursor ions in data-dependent acquisition approaches.
- Aptamers
-
Molecules, for example, oligonucleotides, that bind to a specific target molecule and are used to perform high-throughput proteomics quantification such as in the SomaScan (SomaLogic) platform.
- Protein quantitative trait loci
-
(pQTL). Genomic loci that explain a significant proportion of the variation in the quantities of a protein in a set of biosamples.
- Interactome
-
The whole set of molecular interactions in a particular sample, tissue or cell in a specific organism or phenotype.
- Nuclear magnetic resonance
-
(NMR). A physical phenomenon in which nuclei in a strong constant magnetic field are perturbed by a weak oscillating magnetic field and respond by producing an electromagnetic signal with a frequency characteristic of the magnetic field at the nucleus. This physical phenomenon is used in NMR spectroscopy, which is a technique for determining the structure of organic compounds with applications in lipoprotein profiling and metabolomics.
- Deep neural network
-
A form of an artificial neural network with many hidden layers, used in classification, regression, clustering and other machine learning applications.
- Fine mapping
-
Process by which a trait-associated region from a genome-wide association study is analysed to identify the particular genetic variants that are most likely to be causal.
- Flux balance analysis
-
Mathematical method for simulating metabolic processes in genome-scale reconstructions of metabolic networks.
Rights and permissions
About this article
Cite this article
Joshi, A., Rienks, M., Theofilatos, K. et al. Systems biology in cardiovascular disease: a multiomics approach. Nat Rev Cardiol 18, 313–330 (2021). https://doi.org/10.1038/s41569-020-00477-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41569-020-00477-1
This article is cited by
-
Integration of polygenic and gut metagenomic risk prediction for common diseases
Nature Aging (2024)
-
Nano–Bio Interactions: Exploring the Biological Behavior and the Fate of Lipid-Based Gene Delivery Systems
BioDrugs (2024)
-
Metabolic systems approaches update molecular insights of clinical phenotypes and cardiovascular risk in patients with homozygous familial hypercholesterolemia
BMC Medicine (2023)
-
Identification of m7G regulator-mediated RNA methylation modification patterns and related immune microenvironment regulation characteristics in heart failure
Clinical Epigenetics (2023)
-
‘Multi-omics’ data integration: applications in probiotics studies
npj Science of Food (2023)
