Expressed genome molecular signatures of heart failure
-
Choong Chin Liew
Abstract
Traditional gene expression studies typically focus on one or a few genes of interest. An important limitation of single-gene studies is that they present a portrait of disease that is essentially static. However, disease is a dynamic process, driven by a combination of genetic, epigenetic and environmental factors. Recently, genomic technologies have permitted better characterization of the dynamic aspect of disease progression. Genome-wide expression profiles of cardiovascular diseases, heart failure in particular, using microarrays have been published and are providing new insights into this complex disease. Tissue biopsies required for traditional microarray studies, however, are often invasive and not readily available. By contrast, blood samples are relatively non-invasive and are readily available. In a number of recent studies, blood cells appear to be a viable substitute for tissue biopsy. Blood cells have the ability to mirror the body's tissues and organs in health and disease; thus, we hypothesize that blood cells can indicate at the molecular level the presence of disease. Here we review microarray gene expression profiling of blood RNA for a number of different diseases. Sieving through gene expression molecular signatures has identified groups of genes characteristic of each and has identified biomarkers associated with specific diseases.
References
1 Nabel EG. Genomic Medicine: cardiovascular disease. N Engl J Med 2003; 349: 60–72. 10.1056/NEJMra035098Search in Google Scholar
2 Goldstein JL, Brown MS. The LDL receptor defect in familial hypercholesterolemia: implications for pathogenesis and therapeutics. Med Clin N Am 1982; 66: 335–62. 10.1016/S0025-7125(16)31424-9Search in Google Scholar
3 Liew CC, Hwang DM, Fung YW, Laurenssen C, Cukerman E, Lee CY. A catalogue of genes in the cardiovascular system as identified by expressed sequence tags. Proc Natl Acad Sci USA 1994; 91: 10645–9. 10.1073/pnas.91.22.10645Search in Google Scholar
4 Hwang DM, Dempsey AA, Wang RX, Rezvani M, Barrans JD, Dai KS, et al. A genome-based resource for molecular cardiovascular medicine: toward a compendium of cardiovascular genes. Circulation 1997; 96: 4146–203. 10.1161/01.CIR.96.12.4146Search in Google Scholar
5 Dempsey A, Dzau VJ, Liew CC. Cardiovascular genomics: estimating the total number of genes expressed in the human cardiovascular system. J Mol Cell Cardiol 2001; 33: 1879–86. 10.1006/jmcc.2001.1447Search in Google Scholar
6 Liew CC, Dzau VJ. Human Genome Project and cardiovascular disease genes. In: Chien K, editor. Molecular basis of cardiovascular disease: a companion to Braunwald's heart disease, 2nd ed. Philadelphia: WB Saunders, 2004:16–38. Search in Google Scholar
7 Dempsey AA, Pabalan N, Tang HC, Liew CC. Organization of human cardiovascular expressed genes on chromosomes 21 and 22. J Mol Cell Cardiol 2001; 33: 587–91. 10.1006/jmcc.2000.1335Search in Google Scholar
8 Barrans JD, Ip J, Lam CW, Hwang IL, Dzau VJ, Liew CC. Chromosomal distribution of the human cardiovascular transcriptome. Genomics 2003; 81: 520–5. 10.1016/S0888-7543(03)00008-9Search in Google Scholar
9 Yager TD, Dempsey AA, Tang H, Stamatiou D, Chao S, Marshall KW, et al. First comprehensive mapping of cartilage transcripts to the human genome. Genomics 2004; 84: 524–35. 10.1016/j.ygeno.2004.05.006Search in Google Scholar PubMed
10 Holloway AJ, Van Laar RK, Tothill RW, Bowtell DD. Options available – from start to finish – for obtaining data from DNA microarrays. Nat Genet 2002; 32S: 481–9. 10.1038/ng1030Search in Google Scholar PubMed
11 Gibbons GH, Liew CC, Goodarzi MO, Rotter JI, Hsueh WA, Siragy HM, et al. Genetic markers: progress and potential for cardiovascular disease. Circulation 2004; 109(Suppl 1): IV47–58. 10.1161/01.CIR.0000133440.86427.26Search in Google Scholar PubMed
12 Liew CC, Dzau V. Molecular genetics and genomics of heart failure. Nat Rev Genet 2004; 5: 1–16. Search in Google Scholar
13 Friddle CJ, Koga T, Rubin EM, Bristow J. Expression profiling reveals distinct sets of genes altered during induction and regression of cardiac hypertrophy. Proc Natl Acad Sci USA 2000; 97: 6745–50. 10.1073/pnas.100127897Search in Google Scholar
14 Yang J, Moravec CS, Sussman MA, DiPaola NR, Fu D, Hawthorn L, et al. Decreased SLIM1 expression and increased gelsolin expression in failing human hearts measured by high density oligonucleotide arrays. Circulation 2000; 102: 3046–52. 10.1161/01.CIR.102.25.3046Search in Google Scholar
15 Barrans JD, Stamatiou D, Liew CC. Construction of a human cardiovascular cDNA microarray: portrait of the failing heart. Biochem Biophys Res Commun 2001; 280: 964–69. 10.1006/bbrc.2000.4137Search in Google Scholar
16 Tan FL Moravec CS, Li J, Apperson-Hansen C, McCarthy PM, Young JB, et al. The gene expression fingerprint of human heart failure. Proc Natl Acad Sci USA 2002; 99: 11387–92. 10.1073/pnas.162370099Search in Google Scholar
17 Boheler KR, Volkova M, Morrell C, Garg R, Zhu Y, Margulies K, et al. Sex- and age-dependent human transcriptome variability: implications for chronic heart failure. Proc Natl Acad Sci USA 2003; 100: 2754–9. 10.1073/pnas.0436564100Search in Google Scholar
18 Steenman M, Chen YW, Le Cunff M, Lamirault G, Varro A, Hoffman E, et al. Transcriptomal analysis of failing and nonfailing human hearts. Physiol Genomics 2003; 12: 97–112. 10.1152/physiolgenomics.00148.2002Search in Google Scholar
19 Xu J, Stolk JA, Zhang X, Silva SJ, Houghton RL, Matsumura M, et al. Identification of differentially expressed genes in human prostate cancer using subtraction and microarray. Cancer Res 2000; 60: 1677–82. Search in Google Scholar
20 Hwang JJ, Allen PD, Tseng GC, Lami CW, Lameh F, Dzau VJ, et al. Microarray gene expression profiles in dilated and hypertrophic cardiomyopathic end-stage heart failure. Physiol Genomics 2002; 10: 31–44. 10.1152/physiolgenomics.00122.2001Search in Google Scholar
21 Schonberger J, Seidman CE. Many roads lead to a broken heart: the genetics of dilated cardiomyopathy. Am J Hum Genet 2001; 69: 249–60. 10.1086/321978Search in Google Scholar
22 Seidman C. Hypertrophic cardiomyopathy: from man to mouse. J Clin Invest 2000; 106: S9–13. Search in Google Scholar
23 Wigle ED, Rakowski H, Kimball BP, Williams WG. Hypertrophic cardiomyopathy: clinical spectrum and treatment. Circulation 1995; 92: 1680–92. 10.1161/01.CIR.92.7.1680Search in Google Scholar
24 Grzeskowiak R, Witt H, Drungowski M, Thermann R, Hennig S, Perrot A, et al. Expression profiling of human idiopathic dilated cardiomyopathy. Cardiovasc Res 2003; 59: 400–11. 10.1016/S0008-6363(03)00426-7Search in Google Scholar
25 Kapoun AM, Liang F, O'Young G, Damm DL, Quon D, White RT, et al. B-Type natriuretic peptide exerts broad functional opposition to transforming growth factor-β in primary human cardiac fibroblasts: fibrosis, myofibroblast conversion, proliferation, and inflammation. Circ Res 2004; 94: 453–61. 10.1161/01.RES.0000117070.86556.9FSearch in Google Scholar
26 Dobson JG, Fray J, Leonard JL, Pratt RE. Molecular mechanisms of reduced β-adrenergic signaling in the aged heart as revealed by genomic profiling. Physiol Genomics 2003; 15: 142–7. 10.1152/physiolgenomics.00076.2003Search in Google Scholar
27 Stanton LW, Garrard LJ, Damm D, Garrick BL, Lam A, Kapoun AM, et al. Altered patterns of gene expression in response to myocardial infarction. Circ Res 2000; 86: 939–45. 10.1161/01.RES.86.9.939Search in Google Scholar
28 Ogawa M. Differentiation and proliferation of hematopoietic stem cells. Blood 1993; 81: 2844–53. 10.1182/blood.V81.11.2844.2844Search in Google Scholar
29 Liew CC. Method for the detection of gene transcriptsin blood and uses thereof. US Patent application 2002000268730, 1999. Search in Google Scholar
30 Ma J, Liew CC. Gene profiling identifies secreted protein transcripts from peripheral blood cells in coronary artery disease. J Mol Cell Cardiol 2003; 35: 993–8. 10.1016/S0022-2828(03)00179-2Search in Google Scholar
31 Barnes MG, Aronow BJ, Luyrink LK, Moroldo MB, Pavlidis P, Passo MH. Gene expression in juvenile arthritis and spondyloarthropathy: pro-angiogenic ELR+ chemokine genes relate to course of arthritis. Rheumatology (Oxf) 2004; 43: 973–9. 10.1093/rheumatology/keh224Search in Google Scholar PubMed
32 Okuda T, Sumiya T, Mizutani K, Tago N, Miyata T, Tanabe T, et al. Analyses of differential gene expression in genetic hypertensive rats by microarray. Hypertens Res Clin Exp 2002; 25: 249–55. 10.1291/hypres.25.249Search in Google Scholar PubMed
33 Chon H, Gaillard CA, van der Meijden BB, Dijstelbloem HM, Kraaijenhagen RJ, van Leenen D, et al. Broadly altered gene expression in blood leukocytes in essential hypertension is absent during treatment. Hypertension 2004; 43: 947–51. 10.1161/01.HYP.0000123071.35142.72Search in Google Scholar PubMed
34 Bull TM, Coldren CD, Moore M, Sotto-Santiago SM, Pham DV, Nana-Sinkam SP, et al. Gene microarray analysis of peripheral blood cells in pulmonary arterial hypertension. Am J Respir Crit Care Med 2004; 170: 911–9. 10.1164/rccm.200312-1686OCSearch in Google Scholar PubMed
35 DePrimo SE, Wong LM, Khatry DB, Nicholas SL, Manning WC, Smolich BD, et al. Expression profiling of blood samples from an SU5416 Phase III metastatic colorectal cancer clinical trial: a novel strategy for biomarker identification. BMC Cancer 2003; 7: 3. 10.1186/1471-2407-3-3Search in Google Scholar PubMed PubMed Central
36 Whistler T, Unger ER, Nisenbaum R, Vernon SD. Integration of gene expression, clinical, and epidemiologic data to characterize chronic fatigue syndrome. J Transl Med 2003; 1: 10. 10.1186/1479-5876-1-10Search in Google Scholar
37 Tang Y, Lu A, Aronow BJ, Sharp FR. Blood genomic responses differ after stroke, seizures, hypoglycemia, and hypoxia: blood genomic fingerprints of disease. Ann Neurol 2001; 50: 699–707. 10.1002/ana.10042Search in Google Scholar
38 Tang Y, Nee AC, Lu A, Ran R, Sharp FR. Blood genomic expression profile for neuronal injury. J Cereb Blood Flow Metab 2003; 23: 310–9. 10.1097/01.WCB.0000048518.34839.DESearch in Google Scholar
39 Feezor RJ, Baker HV, Mindrinos M, Hayden D, Tannahill CL, Brownstein BH, et al. Inflammation and Host Response to Injury, Large-Scale Collaborative Research Program (2004). Whole blood and leukocyte RNA isolation for gene expression analyses. Physiol Genomics 2004; 19: 247–54. Search in Google Scholar
40 Morello F, De Bruin TW, Rotter JI, Pratt RE, Van Der Kallen CJ, Hladik JA, et al. Differential gene expression of blood-derived cell lines in familial combined hyperlipemia. Arterioscler Thromb Vasc Biol 2004; 24: 2149–54. 10.1161/01.ATV.0000145978.70872.63Search in Google Scholar
41 Eurlings PM, Van der Kallen CJ, Geurts JM, Kouwenberg P, Boeckx WD, De Bruin TW. Identification of differentially expressed genes in subcutaneous adipose tissue from subjects with familial combined hyperlipidemia. J Lipid Res 2002; 43: 930–5. 10.1016/S0022-2275(20)30467-3Search in Google Scholar
42 Tsuang MT, Nossova N, Yager T, Tsuang MM, Guo SC, Shyu KG, et al. Assessing the validity of blood-based gene expression profiles for the classification of schizophrenia and bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 2005; 133B: 1–5. 10.1002/ajmg.b.30161Search in Google Scholar PubMed
© by Walter de Gruyter Berlin New York
Articles in the same Issue
- Second Santorini Conference “From Human Genetic Variations to Prediction of Risks and Responses to Drugs and to the Environment”
- Expressed genome molecular signatures of heart failure
- Understanding hyperlipidemia and atherosclerosis: lessons from genetically modified apoe and ldlr mice
- Alcohol and gene interactions
- β-Carotene stimulates chemotaxis of human endothelial progenitor cells
- Effect of interferon-γ, interleukin-10, lipopolysaccharide and tumor necrosis factor-α on chitotriosidase synthesis in human macrophages
- Two immunochemical assays to measure advanced glycation end-products in serum from dialysis patients
- Apolipoprotein E haplotyping by denaturing high-performance liquid chromatography
- Both core and terminal glycosylation alter epitope expression in thyrotropin and introduce discordances in hormone measurements
- Do we measure bilirubin correctly anno 2005?
- Differences in mortality on the basis of laboratory parameters in an unselected population at the Emergency Department
- An Italian program of external quality control for quantitative assays based on real-time PCR with Taq-Man™ probes
- A reference material for traceability of aspartate aminotransferase (AST) results
- Harmonization of the Bayer ADVIA Centaur and Abbott AxSYM automated B-type natriuretic peptide assay in patients on hemodialysis
- Multicenter evaluation of the analytical and clinical performance of the Elecsys ® S100 immunoassay in patients with malignant melanoma
- Guidelines for sampling, measuring and reporting ionized magnesium in undiluted serum, plasma or blood: International Federation of Clinical Chemistry and Laboratory Medicine (IFCC): IFCC Scientific Division, Committee on Point of Care Testing
- Evaluation of the Quantase™ neonatal immunoreactive trypsinogen (IRT) screening assay for cystic fibrosis
Articles in the same Issue
- Second Santorini Conference “From Human Genetic Variations to Prediction of Risks and Responses to Drugs and to the Environment”
- Expressed genome molecular signatures of heart failure
- Understanding hyperlipidemia and atherosclerosis: lessons from genetically modified apoe and ldlr mice
- Alcohol and gene interactions
- β-Carotene stimulates chemotaxis of human endothelial progenitor cells
- Effect of interferon-γ, interleukin-10, lipopolysaccharide and tumor necrosis factor-α on chitotriosidase synthesis in human macrophages
- Two immunochemical assays to measure advanced glycation end-products in serum from dialysis patients
- Apolipoprotein E haplotyping by denaturing high-performance liquid chromatography
- Both core and terminal glycosylation alter epitope expression in thyrotropin and introduce discordances in hormone measurements
- Do we measure bilirubin correctly anno 2005?
- Differences in mortality on the basis of laboratory parameters in an unselected population at the Emergency Department
- An Italian program of external quality control for quantitative assays based on real-time PCR with Taq-Man™ probes
- A reference material for traceability of aspartate aminotransferase (AST) results
- Harmonization of the Bayer ADVIA Centaur and Abbott AxSYM automated B-type natriuretic peptide assay in patients on hemodialysis
- Multicenter evaluation of the analytical and clinical performance of the Elecsys ® S100 immunoassay in patients with malignant melanoma
- Guidelines for sampling, measuring and reporting ionized magnesium in undiluted serum, plasma or blood: International Federation of Clinical Chemistry and Laboratory Medicine (IFCC): IFCC Scientific Division, Committee on Point of Care Testing
- Evaluation of the Quantase™ neonatal immunoreactive trypsinogen (IRT) screening assay for cystic fibrosis