Understanding systemic sclerosis through gene expression profiling

Current Opinion in Rheumatology

Volumn 19, Issue 6, 2007, Pages 561-567

  Pendergrass, Sarah A ^a   Whitfield, Michael L ^a   Gardner, Humphrey ^b  

^a DARTMOUTH MEDICAL SCHOOL   (United States)

^b NOVARTIS INSTITUTES FOR BIOMEDICAL RESEARCH   (United States)

Author keywords

Gene expression profiling; Genomics; Microarrays; Scleroderma

Indexed keywords

MESSENGER RNA; PLATELET DERIVED GROWTH FACTOR RECEPTOR;

DNA FINGERPRINTING; EXTRACELLULAR MATRIX; FIBROBLAST; GENE EXPRESSION; GENE MAPPING; GENETIC ANALYSIS; HUMAN; IMMUNE SYSTEM; LONGITUDINAL STUDY; MICROARRAY ANALYSIS; NONHUMAN; PATHOGENESIS; PATHOPHYSIOLOGY; PRIORITY JOURNAL; REVIEW; SINGLE NUCLEOTIDE POLYMORPHISM; SKIN BIOPSY; STANDARDIZATION; SYSTEMIC SCLEROSIS;

ANIMALS; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SCLERODERMA, SYSTEMIC;

EID: 34948839371     PISSN: 10408711     EISSN: None     Source Type: Journal    
DOI: 10.1097/BOR.0b013e3282f00375     Document Type: Review

References (71)

1. van de Vijver MJ, He YD, van't Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347:1999-2009.
2. Glas AM, Floore A, Delahaye LJ, et al. Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 2006; 7:278. Validation of a custom microarray for breast cancer diagnosis and prognostication.
3. Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403:503-511.
4. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A 2001; 98:13790-13795.
5. Garber ME, Troyanskaya OG, Schluens K, et al. Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci U S A 2001; 98:13784-13789.
6. Hu Z, Fan C, Oh DS, et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 2006; 7:96.
7. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000; 406:747-752.
8. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001; 98:10869-10874.
9. Sorlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A 2003; 100:8418-8423.
10. de Reynies A, Geromin D, Cayuela JM, et al. Comparison of the latest commercial short and long oligonucleotide microarray technologies. BMC Genomics 2006; 7:51.
11. La Paz JL, Esteve T, Pla M. Comparison of real-time PCR detection chemistries and cycling modes using Mon810 event-specific assays as model. J Agric Food Chem 2007; 55:4312-4318.
12. Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO: mining tens of millions of expression profiles: database and tools update. Nucleic Acids Res 2007; 35:D760-D765.
13. Parkinson H, Kapushesky M, Shojatalab M, et al. ArrayExpress: a public database of microarray experiments and gene expression profiles. Nucleic Acids Res 2007; 35:D747-D750.
14. Tan FK, Zhou X, Mayes MD, et al. Signatures of differentially regulated interferon gene expression and vasculotrophism in the peripheral blood cells of systemic sclerosis patients. Rheumatology (Oxford) 2006; 45: 694-702.
15. York MR, Nagai T, Mangini AJ, et al. A macrophage marker, Siglec-1, is increased on circulating monocytes in patients with systemic sclerosis and induced by type I interferons and toll-like receptor agonists. Arthritis Rheum 2007; 56:1010-1020.
16. 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.
17. Batliwalla FM, Li W, Ritchlin CT, et al. Microarray analyses of peripheral blood cells identifies unique gene expression signature in psoriatic arthritis. Mol Med 2005; 11:21-29.
18. Batliwalla FM, Baechler EC, Xiao X, et al. Peripheral blood gene expression profiling in rheumatoid arthritis. Genes Immun 2005; 6:388-397.
19. Baechler EC, Batliwalla FM, Karypis G, et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci U S A 2003; 100:2610-2615.
20. Chaussabel D, Allman W, Mejias A, et al. Analysis of significance patterns identifies ubiquitous and disease-specific gene-expression signatures in patient peripheral blood leukocytes. Ann N Y Acad Sci 2005; 1062:146-154.
21. Maas K, Chen H, Shyr Y, et al. 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.
22. Olsen N, Sokka T, Seehorn CL, et al. A gene expression signature for recent onset rheumatoid arthritis in peripheral blood mononuclear cells. Ann Rheum Dis 2004; 63:1387-1392.
23. Palmer C, Diehn M, Alizadeh AA, Brown PO. Cell-type specific gene expression profiles of leukocytes in human peripheral blood. BMC Genomics 2006; 7:115.
24. Baechler EC, Batliwalla FM, Karypis G, et al. Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation. Genes Immun 2004; 5:347-353.
25. Gardner H, Shearstone JR, Bandaru R, et al. Gene profiling of scleroderma skin reveals robust signatures of disease that are imperfectly reflected in the transcript profiles of explanted fibroblasts. Arthritis Rheum 2006; 54:1961-1973. Affymetrix study of SSc patient biopsies and explanted fibroblasts with the largest patient number examined to date.
26. Whitfield ML, Finlay DR, Murray JI, et al. Systemic and cell type-specific gene expression patterns in scleroderma skin. Proc Natl Acad Sci U S A 2003; 100:12319-12324.
27. Trcka J, Kunz M. Functional genome and proteome analyses of cutaneous autoimmune diseases. Curr Pharm Des 2006; 12:3787-3798.
28. Oestreicher JL, Walters IB, Kikuchi T, et al. Molecular classification of psoriasis disease-associated genes through pharmacogenomic expression profiling. Pharmacogenomics J 2001; 1:272-287.
29. Zhou X, Krueger JG, Kao MC, et al. Novel mechanisms of T-cell and dendritic cell activation revealed by profiling of psoriasis on the 63,100-element oligonucleotide array. Physiol Genomics 2003; 13:69-78.
30. Rinn JL, Bondre C, Gladstone HB, et al. Anatomic demarcation by positional variation in fibroblast gene expression programs. PLoS Genet 2006; 2:e119.
31. Szulgit G, Rudolph R, Wandel A, et al. Alterations in fibroblast alpha1beta1 integrin collagen receptor expression in keloids and hypertrophic scars. J Invest Dermatol 2002; 118:409-415.
32. Leask A, Abraham DJ, Finlay DR, et al. Dysregulation of transforming growth factor beta signaling in scleroderma: overexpression of endoglin in cutaneous scleroderma fibroblasts. Arthritis Rheum 2002; 46:1857-1865.
33. Zhou X, Tan FK, Xiong M, et al. Monozygotic twins clinically discordant for scleroderma show concordance for fibroblast gene expression profiles. Arthritis Rheum 2005; 52:3305-3314.
34. Tan FK, Hildebrand BA, Lester MS, et al. Classification analysis of the transcriptosome of nonlesional cultured dermal fibroblasts from systemic sclerosis patients with early disease. Arthritis Rheum 2005; 52:865-876.
35. Zhou X, Tan FK, Xiong M, et al. Systemic sclerosis (scleroderma): specific autoantigen genes are selectively overexpressed in scleroderma fibroblasts. J Immunol 2001; 167:7126-7133.
36. Pannu J, Gardner H, Shearstone JR, et al. Increased levels of transforming growth factor beta receptor type I and up-regulation of matrix gene program: a model of scleroderma. Arthritis Rheum 2006; 54:3011-3021.
37. Segal E, Friedman N, Koller D, Regev A. A module map showing conditional activity of expression modules in cancer. Nat Genet 2004; 36:1090-1098.
38. Segal E, Shapira M, Regev A, et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 2003; 34:166-176.
39. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005; 102:15545-15550.
40. Mootha VK, Lindgren CM, Eriksson KF, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003; 34:267-273.
41. Efron B, Tibshirani R. On testing for the significance sets of genes. Ann Appl Stat (in press).
42. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001; 98:5116-5121.
43. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc 1995; B57:289-300.
44. Bennett L, Palucka AK, Arce E, et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med 2003; 197:711-723.
45. Sfikakis PP, McCune BK, Tsokos M, et al. Immunohistological demonstration of transforming growth factor-beta isoforms in the skin of patients with systemic sclerosis. Clin Immunol Immunopathol 1993; 69:199-204.
46. LeRoy EC, Trojanowska MI, Smith EA. Cytokines and human fibrosis. Eur Cytokine Netw 1990; 1:215-219.
47. Kikuchi K, Hartl CW, Smith EA, et al. Direct demonstration of transcriptional activation of collagen gene expression in systemic sclerosis fibroblasts: insensitivity to TGF beta 1 stimulation. Biochem Biophys Res Commun 1992; 187:45-50.
48. Kawakami T, Ihn H, Xu W, et al. Increased expression of TGF-beta receptors by scleroderma fibroblasts: evidence for contribution of autocrine TGF-beta signaling to scleroderma phenotype. J Invest Dermatol 1998; 110:47-51.
49. Jimenez SA, Hitraya E, Varga J. Pathogenesis of scleroderma. Collagen Rheum Dis Clin North Am 1996; 22:647-674.
50. Gabrielli A, Di Loreto C, Taborro R, et al. Immunohistochemical localization of intracellular and extracellular associated TGF beta in the skin of patients with systemic sclerosis (scleroderma) and primary Raynaud's phenomenon. Clin Immunol Immunopathol 1993; 68:340-349.
51. Falanga V, Gerhardt CO, Dasch JR, et al. Skin distribution and differential expression of transforming growth factor beta 1 and beta 2. J Dermatol Sci 1992; 3:131-136.
52. Giusti B, Fibbi G, Margheri F, et al. A model of antiangiogenesis: differential transcriptosome profiling of microvascular endothelial cells from diffuse systemic sclerosis patients. Arthritis Res Ther 2006; 8:R115.
53. Baroni SS, Santillo M, Bevilacqua F, et al. Stimulatory autoantibodies to the PDGF receptor in systemic sclerosis. N Engl J Med 2006; 354:2667-2676.
54. Gu J, Iyer VR. PI3K signaling and miRNA expression during the response of quiescent human fibroblasts to distinct proliferative stimuli. Genome Biol 2006; 7:R42.
55. Lakos G, Takagawa S, Varga J. Animal models of scleroderma. Methods Mol Med 2004; 102:377-393.
56. Siracusa LD, McGrath R, Ma Q, et al. A tandem duplication within the fibrillin 1 gene is associated with the mouse tight skin mutation. Genome Res 1996; 6:300-313.
57. Baxter RM, Crowell TP, McCrann ME, et al. Analysis of the tight skin (Tsk1/+) mouse as a model for testing antifibrotic agents. Lab Invest 2005; 85:1199-1209.
58. Christner PJ, Peters J, Hawkins D, et al. The tight skin 2 mouse: an animal model of scleroderma displaying cutaneous fibrosis and mononuclear cell infiltration. Arthritis Rheum 1995; 38:1791-1798.
59. Zhou L, Askew D, Wu C, Gilliam AC. Cutaneous gene expression by DNA microarray in murine sclerodermatous graft-versus-host disease, a model for human scleroderma. J Invest Dermatol 2007; 127:281-292.
60. Fleischmajer R, Perlish JS, Reeves JR. Cellular infiltrates in scleroderma skin. Arthritis Rheum 1977; 20:975-984.
61. Sato S, Fujimoto M, Hasegawa M, et al. Altered B lymphocyte function induces systemic autoimmunity in systemic sclerosis. Mol Immunol 2004; 41:1123-1133.
62. Fujimoto M, Sato S. B lymphocytes and systemic sclerosis. Curr Opin Rheumatol 2005; 17:746-751.
63. Hasegawa M, Fujimoto M, Takehara K, Sato S. Pathogenesis of systemic sclerosis: altered B cell function is the key linking systemic autoimmunity and tissue fibrosis. J Dermatol Sci 2005; 39:1-7.
64. Kalogerou A, Gelou E, Mountantonakis S, et al. Early T cell activation in the skin from patients with systemic sclerosis. Ann Rheum Dis 2005; 64:1233-1235.
65. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, et al. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 2007; 9:166-180.
66. Elfilali A, Lair S, Verbeke C, et al. ITTACA: a new database for integrated tumor transcriptome array and clinical data analysis. Nucleic Acids Res 2006; 34:D613-D616.
67. Rhodes DR, Yu J, Shanker K, et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 2004; 6:1-6.
68. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 2006; 355:560-569. In breast cancer, diagnostic tests based on gene expression are becoming commonplace. Different laboratories have identified different, nonoverlapping prognostic signatures that show significant agreement in outcome predictions for patients with breast cancer.
69. Nuyten DS, Kreike B, Hart AA, et al. Predicting a local recurrence after breast-conserving therapy by gene expression profiling. Breast Cancer Res 2006; 8:R62.
70. Chang HY, Nuyten DS, Sneddon JB, et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci U S A 2005; 102:3738-3743.
71. Chang HY, Sneddon JB, Alizadeh AA, et al. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol 2004; 2:E7.