Abstract
To determine if individuals with metabolic disorders possess unique gene expression profiles, we compared transcript levels in peripheral blood from patients with coronary artery disease (CAD), type 2 diabetes (T2D) and their precursor state, metabolic syndrome to those of control (CTRL) subjects and subjects with rheumatoid arthritis (RA). The gene expression profile of each metabolic state was distinguishable from CTRLs and correlated with other metabolic states more than with RA. Of note, subjects in the metabolic cohorts overexpressed gene sets that participate in the innate immune response. Genes involved in activation of the pro-inflammatory transcription factor, NF-κB, were overexpressed in CAD whereas genes differentially expressed in T2D have key roles in T-cell activation and signaling. Reverse transcriptase PCR validation confirmed microarray results. Furthermore, several genes differentially expressed in human metabolic disorders have been previously shown to participate in inflammatory responses in murine models of obesity and T2D. Taken together, these data demonstrate that peripheral blood from individuals with metabolic disorders display overlapping and non-overlapping patterns of gene expression indicative of unique, underlying immune processes.
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Stumvoll M, Goldstein BJ, van Haeften TW . Type 2 diabetes: principles of pathogenesis and therapy. Lancet 2005; 365: 1333–1346.
Centers for Disease Control and Prevention. National Diabetes Fact Sheet: General Information and National Estimates on Diabetes in the United States, 2007. US Department of Health and Human Services, Centers for Disease Control and Prevention: Atlanta, GA, 2008.
Ross R . The pathogenesis of atherosclerosis: a perspective for the 1990s. Nature 1993; 362: 801–809.
Lloyd-Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB, Flegal K et al. Heart disease and stroke statistics--2009 update: a report from the american heart association statistics committee and stroke statistics subcommittee. Circulation 2009; 119: e21–181.
Alberti KGMM, Zimmet P, Shaw J . The metabolic syndrome–a new worldwide definition. Lancet 2005; 366: 1059–1062.
Ford ES . Prevalence of the metabolic syndrome defined by the international diabetes federation among adults in the US. Diabetes Care 2005; 28: 2745–2749.
Galassi A, Reynolds K, He J . Metabolic syndrome and risk of cardiovascular disease: a meta-analysis. Am J Med 2006; 119: 812–819.
Gami AS, Witt BJ, Howard DE, Erwin PJ, Gami LA, Somers VK et al. Metabolic syndrome and risk of incident cardiovascular events and death: a systematic review and meta-analysis of longitudinal studies. J Am Coll Cardiol 2007; 49: 403–414.
Ford ES, Li C, Sattar N . Metabolic syndrome and incident diabetes. Diabetes Care 2008; 31: 1898–1904.
Sattar N, McConnachie A, Shaper AG, Blauw GJ, Buckley BM, de Craen AJ et al. Can metabolic syndrome usefully predict cardiovascular disease and diabetes? Outcome data from two prospective studies. Lancet 2008; 371: 1927–1935.
Meigs JB, Wilson PWF, Fox CS, Vasan RS, Nathan DM, Sullivan LM et al. Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. J Clin Endocrinol Metab 2006; 91: 2906–2912.
Shimabukuro M . Cardiac adiposity and global cardiometabolic risk new concept and clinical implication. Circ J 2009; 73: 27–34.
Zimmet P, Alberti KGMM, Shaw J . Global and societal implications of the diabetes epidemic. Nature 2001; 414: 782–787.
Wellen KE, Hotamisligil GS . Obesity-induced inflammatory changes in adipose tissue. J Clin Invest 2003; 112: 1785–1788.
Martinez FO, Gordon S, Locati M, Mantovani A . Transcriptional profiling of the human monocyte-to-macrophage differentiation and polarization: new molecules and patterns of gene expression. J Immunol 2006; 177: 7303–7311.
Tan Q, Zhao J, Li S, Christiansen L, Kruse TA, Christensen K . Differential and correlation analyses of microarray gene expression data in the CEPH Utah families. Genomics 2008; 92: 94–100.
Gregg JP, Lit L, Baron CA, Hertz-Picciotto I, Walker W, Davis RA et al. Gene expression changes in children with autism. Genomics 2008; 91: 22–29.
Lockstone HE, Harris LW, Swatton JE, Wayland MT, Holland AJ, Bahn S . Gene expression profiling in the adult down syndrome brain. Genomics 2007; 90: 647–660.
Sotiriou C, Pusztai L . Gene-expression signatures in breast cancer. New Engl J Med 2009; 360: 790–800.
Cheok MH, Yang W, Pui C-H, Downing JR, Cheng C, Naeve CW et al. Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells. Nat Genet 2003; 34: 85–90.
Holleman A, Cheok MH, den Boer ML, Yang W, Veerman AJP, Kazemier KM et al. Gene-expression patterns in drug-resistant acute lymphoblastic leukemia cells and response to treatment. N Engl J Med 2004; 351: 533–542.
Aune TM, Maas K, Moore JH, Olsen NJ . Gene expression profiles in human autoimmune disease. Curr pharma des 2003; 9: 1905–1917.
Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. 2003; 100: 2610–2615.
Bomprezzi R, Ringner M, Kim S, Bittner ML, Khan J, Chen Y et al. Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to disease. Hum Mol Genet 2003; 12: 2191–2199.
Maas K, Chen H, Shyr Y, Olsen NJ, Aune T . Shared gene expression profiles in individuals with autoimmune disease and unaffected first-degree relatives of individuals with autoimmune disease. Hum Mol Genet 2005; 14: 1305–1314.
Liu Z, Maas K, Aune TM . Identification of gene expression signatures in autoimmune disease without the influence of familial resemblance. Hum Mol Genet 2006; 15: 501–509.
Maas K, Westfall M, Pietenpol J, Olsen NJ, Aune T . Reduced p53 in peripheral blood mononuclear cells from patients with rheumatoid arthritis is associated with loss of radiation-induced apoptosis. Arthritis rheum 2005; 52: 1047–1057.
Heap G, Trynka G, Jansen R, Bruinenberg M, Swertz M, Dinesen L et al. Complex nature of SNP genotype effects on gene expression in primary human leucocytes. BMC Med Genomics 2009; 2: 1.
Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 2009; 106: 9362–9367.
Torkamani A, Topol EJ, Schork NJ . Pathway analysis of seven common diseases assessed by genome-wide association. Genomics 2008; 92: 265–272.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM et al. Gene ontology: tool for the unification of biology. Nat Genet 2000; 25: 25–29.
Klareskog L, Catrina AI, Paget S . Rheumatoid arthritis. Lancet 2009; 373: 659–672.
Chang CC, Ciubotariu R, Manavalan JS, Yuan J, Colovai AI, Piazza F et al. Tolerization of dendritic cells by TS cells: the crucial role of inhibitory receptors ILT3 and ILT4. Nat Immunol 2002; 3: 237–243.
Shi H, Kokoeva MV, Inouye K, Tzameli I, Yin H, Flier JS . TLR4 links innate immunity and fatty acid-induced insulin resistance. J Clin Invest 2006; 116: 3015–3025.
Berliner JA, Navab M, Fogelman AM, Frank JS, Demer LL, Edwards PA et al. Atherosclerosis: basic mechanisms: oxidation, inflammation, and genetics. Circulation 1995; 91: 2488–2496.
Nishimura S, Manabe I, Nagasaki M, Eto K, Yamashita H, Ohsugi M et al. CD8+ effector T cells contribute to macrophage recruitment and adipose tissue inflammation in obesity. Nat Med 2009; 15: 914–920.
Yessoufou A, Moutairou K, Khan NA . A model of insulin resistance in mice, born to diabetic pregnancy, is associated with alterations of transcription-related genes in pancreas and epididymal adipose tissue. J Obes 2011; 2011: 654967.
Hara T, Nakayama Y . CXCL14 and insulin action. Vitam Horm 2009; 80: 107–123.
Ha E, Yang S-H, Yoo K-I, Chung I-S, Lee M-Y, Bae J-H et al. Interleukin 4 receptor is associated with an increase in body mass index in Koreans. Life Sci 2008; 82: 1040–1043.
Arnett FC, Edworthy SM, Bloch DA, Mcshane DJ, Fries JF, Cooper NS et al. The american rheumatism association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum 1988; 31: 315–324.
Eckel RH, Grundy SM, Zimmet PZ . The metabolic syndrome. Lancet 2005; 365: 1415–1428.
Min JK, Shaw LJ . Noninvasive diagnostic and prognostic assessment of individuals with suspected coronary artery disease: coronary computed tomographic angiography perspective. Circ Cardiovasc Imaging 2008; 1: 270–281.
Saeed A, Sharov V, White J, Li J, Liang W, Bhagabati N et al. TM4: a free, open-source system for microarray data management and analysis. Biotechniques 2003; 34: 374–378.
Reimers M, Carey VJ . Bioconductor: an open source framework for bioinformatics and computational biology. Methods Enzymol 2006; 411: 119–134.
Wang L, Zhang B, Wolfinger RD, Chen X . An integrated approach for the analysis of biological pathways using mixed models. PLoS Genet 2008; 4: e1000115.
Wang L, Chen X, Wolfinger RD, Franklin JL, Coffey RJ, Zhang B . A unified mixed effects model for gene set analysis of time course microarray experiments. Stat Appl Genet Mol Biol 2009; 8 Article 47.
Benjamini Y, Hochberg Y . Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 1995; 57: 289–300.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13: 2498–2504.
Edgar R, Domrachev M, Lash AE . Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002; 30: 207–210.
Acknowledgements
The authors wish to acknowledge Drs James W Thomas, Howard Fuchs, Nancy J Brown and their patients for access to their clinics and providing blood samples. We would also like to acknowledge the Vanderbilt Functional Genomics Shared Resource for technical support with microarrays (Vicky Amann) and RT-PCR experiments (Latha Raju). This work was supported by NIH grants R42 AI053984, T32 GM07347, T32 DK07563 and TL1 RR024978.
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TMA is part owner of ArthroChip, LLC, whose focus is to develop diagnostic tests for autoimmune diseases using gene expression profiles in whole blood.
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Supplementary Information accompanies the paper on Genes and Immunity website
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Grayson, B., Wang, L. & Aune, T. Peripheral blood gene expression profiles in metabolic syndrome, coronary artery disease and type 2 diabetes. Genes Immun 12, 341–351 (2011). https://doi.org/10.1038/gene.2011.13
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DOI: https://doi.org/10.1038/gene.2011.13
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