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Effect of APOB polymorphism rs562338 (G/A) on serum proteome of coronary artery disease patients: a “proteogenomic” approach

  • 1.

    Masson, W. et al. Role of non-statin lipid-lowering therapy in coronary atherosclerosis regression: A meta-analysis and meta-regression. Lipids Health Dis. 19, 1–11 (2020).

    Google Scholar 

  • 2.

    Zdravkovic, S. et al. Heritability of death from coronary heart disease: A 36 year follow up of 20 966 swedish twins. J Inter. Med. 252, 247–254 (2002).

    CAS 

    Google Scholar 

  • 3.

    Fischer, M. et al. Distinct heritable patterns of angiographic coronary artery disease in families with myocardial infarction. Circulation 111, 855–862 (2005).

    PubMed 

    Google Scholar 

  • 4.

    van der Harst, P. & Verweij, N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ. Res. 122, 433–443 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 5.

    Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 6.

    Aulchenko, Y. S. et al. Loci influencing lipid levels and coronary heart disease risk in 16 european population cohorts. Nat. Genet. 41, 47 (2009).

    CAS 
    PubMed 

    Google Scholar 

  • 7.

    Kathiresan, S. et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 56–65 (2009).

    CAS 
    PubMed 

    Google Scholar 

  • 8.

    Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat. Genet. 44, 269–276 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 9.

    Langlois, M. et al. European atherosclerosis society (eas) and the european federation of clinical chemistry and laboratory medicine (eflm) joint consensus initiative. Quantifying atherogenic lipoproteins: Current and future challenges in the era of personalized medicine and very low concentrations of ldl cholesterol. A consensus statement from eas and eflm. Clin. Chem. 64, 1006–1033 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • 10.

    Sharifi, M., Futema, M., Nair, D. & Humphries, S. E. Genetic architecture of familial hypercholesterolaemia. Curr. Cardiol. Rep. 19, 44 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 11.

    Defesche, J. C. et al. Familial hypercholesterolaemia. Nat. Rev. Dis. Primers. 3, 1–20 (2017).

    Google Scholar 

  • 12.

    Jeemon, P., Pettigrew, K., Sainsbury, C., Prabhakaran, D. & Padmanabhan, S. Implications of discoveries from genome-wide association studies in current cardiovascular practice. World J. Cardiol. 3, 230 (2011).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 13.

    Gu, Q. et al. Association between polymorphisms in the apob gene and hyperlipidemia in the chinese yugur population. Braz. J. Med. Biol. Res. 50, 11 (2017).

    Google Scholar 

  • 14.

    Mach, F. et al. 2019 esc/eas guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. Eur. Heart J. 41, 111–188 (2020).

    PubMed 

    Google Scholar 

  • 15.

    Kessler, T., Vilne, B. & Schunkert, H. The impact of genome wide association studies on the pathophysiology and therapy of cardiovascular disease. EMBO Mol. Med. 8, 688–701 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 16.

    Brænne, I. et al. Prediction of causal candidate genes in coronary artery disease loci. Arteroscler. Thromb. Vasc. Biol. 35, 2207–2217 (2015).

    Google Scholar 

  • 17.

    Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A. & Kim, D. Methods of integrating data to uncover genotype–phenotype interactions. Nat. Rev. Genet. 16, 85–97 (2015).

    CAS 
    PubMed 

    Google Scholar 

  • 18.

    Hartiala, J. et al. The genetic architecture of coronary artery disease: Current knowledge and future opportunities. Curr. Atheroscler. Rep. 19, 6 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 19.

    Zhao, Y. et al. Multi-omics integration reveals molecular networks and regulators of psoriasis. BMC Sys. Biol. 13, 1–14 (2019).

    Google Scholar 

  • 20.

    Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Gen. Biol. 18, 1–15 (2017).

    Google Scholar 

  • 21.

    Arneson, D. et al. Multidimensional integrative genomics approaches to dissecting cardiovascular disease. Front Cardiovasc. Med. 4, 8 (2017).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 22.

    Manzoni, C. et al. Genome, transcriptome and proteome: The rise of omics data and their integration in biomedical sciences. Brief Bioinform. 19, 286–302 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • 23.

    Faulkner, S., Dun, M. D. & Hondermarck, H. Proteogenomics: Emergence and promise. Cell. Mol. Life Sci. 72, 953–957 (2015).

    CAS 
    PubMed 

    Google Scholar 

  • 24.

    Thomas, P. G. et al. Recommendations for blood pressure measurement in humans and experimental animals: part 1: Blood pressure measurement in humans: A statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation 111(5), 697–716 (2005).

    Google Scholar 

  • 25.

    Marc-Andr, C. et al. Scientific statement from the American Heart Association. Circulation 124(18), 1996–2019 (2011).

    Google Scholar 

  • 26.

    McKiernan, H. & Danielson, P. Molecular Diagnostics 371–394 (Elsevier, 2017).

    Google Scholar 

  • 27.

    Alyethodi, R. R. et al. T-arms pcr genotyping of snp rs445709131 using thermostable strand displacement polymerase. BMC Res. Note 11, 1–5 (2018).

    Google Scholar 

  • 28.

    Bradford, M. Nothofagus. J. Forest. 73, 248–249 (1979).

    Google Scholar 

  • 29.

    Mirza, M. R. et al. A novel strategy for phosphopeptide enrichment using lanthanide phosphate co-precipitation. Anal. Bioanal. Chem. 404, 853–862 (2012).

    CAS 
    PubMed 

    Google Scholar 

  • 30.

    Beltran-Camacho, L. et al. Identification of the initial molecular changes in response to circulating angiogenic cells-mediated therapy in critical limb ischemia. Stem Cell Res. Ther. 11(1), 1591 (2020).

    Google Scholar 

  • 31.

    Cox, J. et al. Andromeda: A peptide search engine integrated into the maxquant environment. J. Proteome. Res. 10, 1794–1805 (2011).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • 32.

    Cox, J. & Matthias, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotech. 26, 1367–1372 (2008).

    CAS 

    Google Scholar 

  • 33.

    Christian, L. A. et al. Quantitative proteomics reveals subset-specific viral recognition in dendritic cells. Immunity 32(2), 279–289 (2010).

    Google Scholar 

  • 34.

    Green, G. H. & Diggle, P. J. On the operational characteristics of the Benjamini and Hochberg false discovery rate procedure. Stat. Appl. Genet. Mol. Biol. 6, 1302 (2007).

    MathSciNet 
    MATH 

    Google Scholar 

  • 35.

    Szklarczyk, D. et al. The string database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39, 561–568 (2010).

    Google Scholar 

  • 36.

    Kurakin, A. Scale-free flow of life: On the biology, economics, and physics of the cell. Theor. Biol. Medical. Model. 6, 1–28 (2009).

    Google Scholar 

  • 37.

    Gutteridge, A. et al. Nutrient control of eukaryote cell growth: A systems biology study in yeast. BMC Biol. 8, 1–20 (2010).

    Google Scholar 

  • 38.

    Bensimon, A., Heck, A. J. & Aebersold, R. Mass spectrometry-based proteomics and network biology. Annu. Rev. Biochem. 81, 379–405 (2012).

    CAS 
    PubMed 

    Google Scholar 

  • 39.

    Willer, C. J. et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 40, 161–169 (2008).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 40.

    Katzung, B. G. Basic and Clinical Pharmacology, (12th edition) 609–610 (Mc Graw Hill Education, 2012).

    Google Scholar 

  • 41.

    Lettre, G. et al. Genome-wide association study of coronary heart disease and its risk factors in 8,090 african americans: The nhlbi care project. PLoS Genet. 7, e1001300 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 42.

    Choi-Miura, N. H. Quantitative measurement of the novel human plasma protein, ihrp, by sandwich elisa. Biol. Pharm. Bull. 24, 214–217 (2012).

    Google Scholar 

  • 43.

    Sandhu, M. S. et al. Ldl-cholesterol concentrations: A genome-wide association study. The Lancet 371, 483–491 (2008).

    CAS 

    Google Scholar 

  • 44.

    Piñeiro, M. et al. ITIH4 serum concentration increases during acute-phase processes in human patients and is up-regulated by interleukin-6 in hepatocarcinoma hepg2 cells. Biochem. Biophys. Res. Commun. 263, 224–229 (1999).

    PubMed 

    Google Scholar 

  • 45.

    Kashyap, R. S. et al. Inter-α-trypsin inhibitor heavy chain 4 is a novel marker of acute ischemic stroke. Clin. Chim. Acta. 402, 160–163 (2009).

    CAS 
    PubMed 

    Google Scholar 

  • 46.

    Villanueva, J. et al. Differential exoprotease activities confer tumor-specific serum peptidome patterns. J. Clin. Investig. 116, 271–284 (2006).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 47.

    van Winden, A. W. et al. Serum degradome markers for the detection of breast cancer. J. Proteome. Res. 9, 3781–3788 (2010).

    PubMed 

    Google Scholar 

  • 48.

    Song, J. et al. Quantification of fragments of human serum inter-α-trypsin inhibitor heavy chain 4 by a surface-enhanced laser desorption/ionization-based immunoassay. Clin. Chem. 52, 1045–1053 (2006).

    CAS 
    PubMed 

    Google Scholar 

  • 49.

    Tolosano, E. & Altruda, F. Hemopexin: Structure, function, and regulation. DNA Cell Biol. 21, 297–306 (2002).

    CAS 
    PubMed 

    Google Scholar 

  • 50.

    Kumar, S. & Bandyopadhyay, U. Free heme toxicity and its detoxification systems in human. Toxicol. Lett. 157, 175–188 (2005).

    CAS 
    PubMed 

    Google Scholar 

  • 51.

    Tolosano, E., Fagoonee, S., Morello, N., Vinchi, F. & Fiorito, V. Heme scavenging and the other facets of hemopexin. Antioxid. Redox Signal. 12, 305–320 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • 52.

    Balla, J. et al. Heme, heme oxygenase and ferritin in vascular endothelial cell injury. Mol. Nutr. Food Res. 49, 1030–1043 (2005).

    CAS 
    PubMed 

    Google Scholar 

  • 53.

    Vaisar, T. et al. Myeloperoxidase and inflammatory proteins: Pathways for generating dysfunctional high-density lipoprotein in humans. Curr. Atheroscler. Rep. 9, 417–424 (2007).

    CAS 
    PubMed 

    Google Scholar 

  • 54.

    Bratti, L. D. O. S. et al. Complement component 3 (c3) as a biomarker for insulin resistance after bariatric surgery. Clin. Biochem. 50, 529–532 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • 55.

    Ahmad, R. M. H. & Al-Domi, H. A. Complement 3 serum levels as a pro-inflammatory biomarker for insulin resistance in obesity. Diabetes Metab. Syndr. 11, S229–S232 (2017).

    Google Scholar 

  • 56.

    Ursini, F. & Abenavoli, L. The emerging role of complement c3 as a biomarker of insulin resistance and cardiometabolic diseases: Preclinical and clinical evidence. Rev. Recent Clin. Trials. 13, 61–68 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • 57.

    Martin-Fernandez, L. et al. The unravelling of the genetic architecture of plasminogen deficiency and its relation to thrombotic disease. Sci. Rep. 6, 1–7 (2016).

    Google Scholar 

  • 58.

    Mehta, R. & Shapiro, A. Plasminogen deficiency. Haemophilia 14, 1261–1268 (2008).

    CAS 
    PubMed 

    Google Scholar 

  • 59.

    Folsom, A. R. Fibrinolytic factors and atherothrombotic events: Epidemiological evidence. Ann. Med. 32, 85–91 (2000).

    CAS 
    PubMed 

    Google Scholar 

  • 60.

    Stankiewicz, A. M. et al. Social stress increases expression of hemoglobin genes in mouse prefrontal cortex. BMC Neurosci. 15, 1–16 (2014).

    Google Scholar 

  • 61.

    Hristov, M. et al. Importance of cxc chemokine receptor 2 in the homing of human peripheral blood endothelial progenitor cells to sites of arterial injury. Circ. Res. 100, 590–597 (2007).

    CAS 
    PubMed 

    Google Scholar 

  • 62.

    Majumdar, S., Gonder, D., Koutsis, B. & Poncz, M. Characterization of the human beta-thromboglobulin gene: Comparison with the gene for platelet factor 4. J. Biol. Chem. 266, 5785–5789 (1991).

    CAS 
    PubMed 

    Google Scholar 

  • 63.

    Maneerat, Y., Prasongsukarn, K., Benjathummarak, S. & Dechkhajorn, W. Ppbp and defa1/defa3 genes in hyperlipidaemia as feasible synergistic inflammatory biomarkers for coronary heart disease. Lipids Health Dis. 16, 1–12 (2017).

    Google Scholar 

  • 64.

    Rocha, N. A., East, C., Zhang, J. & McCullough, P. A. Apociii as a cardiovascular risk factor and modulation by the novel lipid-lowering agent volanesorsen. Curr. Atheroscler. Rep. 19, 1–9 (2017).

    CAS 

    Google Scholar 

  • 65.

    TGH Group. Working group of the exome sequencing project, national heart, lung, and blood institute, Loss-of-function mutations in apoc3, triglycerides, and coronary disease. N. Engl. J. Med. 371, 22–31 (2014).

    Google Scholar 

  • 66.

    Jørgensen, A. B., Frikke-Schmidt, R., Nordestgaard, B. G. & Tybjærg-Hansen, A. Loss-of-function mutations in apoc3 and risk of ischemic vascular disease. N. Engl. J. Med. 371, 32–41 (2014).

    PubMed 

    Google Scholar 

  • 67.

    Tani, S., Takahashi, A., Nagao, K. & Hirayama, A. Association of lecithin–cholesterol acyltransferase activity measured as a serum cholesterol esterification rate and low-density lipoprotein heterogeneity with cardiovascular risk: A cross-sectional study. Heart Vessels 31, 831–840 (2016).

    PubMed 

    Google Scholar 

  • 68.

    Yokoyama, K., Tani, S., Matsuo, R. & Matsumoto, N. Association of lecithin-cholesterol acyltransferase activity and low-density lipoprotein heterogeneity with atherosclerotic cardiovascular disease risk: A longitudinal pilot study. BMC Cardiovasc. Disord. 18, 1–10 (2018).

    Google Scholar 

  • 69.

    Ahmed, M., Jadhav, A., Hassan, A. & Meng, Q. H. Acute phase reactants as novel predictors of cardiovascular disease. Int. Sch. Res. Notices. 2012, 1–8 (2012).

    Google Scholar 

  • 70.

    Vélez, P. & García, A. Platelet proteomics in cardiovascular diseases. Transl. Proteom. 7, 15–29 (2015).

    Google Scholar 

  • 71.

    Sharony, R. et al. Protein targets of inflammatory serine proteases and cardiovascular disease. J. Inflamm. 7, 1–17 (2010).

    Google Scholar 

  • 72.

    Granger, D. N., Vowinkel, T. & Petnehazy, T. Modulation of the inflammatory response in cardiovascular disease. Hypertension 43, 924–931 (2004).

    CAS 
    PubMed 

    Google Scholar 

  • 73.

    Mügge, A. et al. Mechanisms of contraction induced by human leukocytes in normal and atherosclerotic arteries. Circ. Res. 69, 871–880 (1991).

    PubMed 

    Google Scholar 

  • 74.

    Marcondes, S. & Antunes, E. The plasma and tissue kininogen-kallikrein-kinin system: Role in the cardiovascular system. Curr. Med. Chem. Cardiovasc. Hematol. Agents. 3, 33–44 (2005).

    CAS 
    PubMed 

    Google Scholar 

  • 75.

    Pejler, G., Rönnberg, E., Waern, I. & Wernersson, S. Mast cell proteases: Multifaceted regulators of inflammatory disease. Blood 115, 4981–4990 (2010).

    CAS 
    PubMed 

    Google Scholar 

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