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Molecular Subsets in the Gene Expression Signatures of Scleroderma Skin
Open accessAbstract
BACKGROUND: Scleroderma is a clinically heterogeneous disease with a complex phenotype. The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production. METHODOLOGY AND FINDINGS: We analyzed the genome-wide patterns of gene expression with DNA microarrays in skin biopsies from distinct scleroderma subsets including 17 patients with systemic sclerosis (SSc) with diffuse scleroderma (dSSc), 7 patients with SSc with limited scleroderma (lSSc), 3 patients with morphea, and 6 healthy controls. 61 skin biopsies were analyzed in a total of 75 microarray hybridizations. Analysis by hierarchical clustering demonstrates nearly identical patterns of gene expression in 17 out of 22 of the forearm and back skin pairs of SSc patients. Using this property of the gene expression, we selected a set of 'intrinsic' genes and analyzed the inherent data-driven groupings. Distinct patterns of gene expression separate patients with dSSc from those with lSSc and both are easily distinguished from normal controls. Our data show three distinct patient groups among the patients with dSSc and two groups among patients with lSSc. Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program. The intrinsic groups are statistically significant (p<0.001) and each has been mapped to clinical covariates of modified Rodnan skin score, interstitial lung disease, gastrointestinal involvement, digital ulcers, Raynaud's phenomenon and disease duration. We report a 177-gene signature that is associated with severity of skin disease in dSSc. CONCLUSIONS AND SIGNIFICANCE: Genome-wide gene expression profiling of skin biopsies demonstrates that the heterogeneity in scleroderma can be measured quantitatively with DNA microarrays. The diversity in gene expression demonstrates multiple distinct gene expression programs in the skin of patients with scleroderma.
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Cites
Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method
Livak, K. J., & Schmittgen, T. D. (2001) · Methods · doi:10.1006/meth.2001.1262 · Cited by 180,860
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Molecular portraits of human breast tumours
Perou, C. M., Sørlie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., Rees, C. A., Pollack, J. R., Ross, D. T., Johnsen, H., Akslen, L. A., et al. (2000) · Nature · doi:10.1038/35021093 · Cited by 16,261
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,166
Sørlie, T., Perou, C. M., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.191367098 · Cited by 10,938
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F., Ben Miled, S., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,803
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Heid, C. A., Stevens, J., Livak, K. J., & Williams, P. M. (1996) · Genome Research · doi:10.1101/gr.6.10.986 · Cited by 6,174
Research works printed but not linked: 13
- MD Mayes, 1998, Classification and epidemiology of scleroderma., Semin Cutan Med Surg, 17, 22, 10.1016/S1085-5629(98)80058-8 · doi:10.1016/s1085-5629(98)80058-8
- VD Steen, 2000, Severe organ involvement in systemic sclerosis with diffuse scleroderma., Arthritis Rheum, 43, 2437, 10.1002/1529-0131(200011)43:11<2437::AID-ANR10>3.0.CO;2-U · doi:10.1002/1529-0131(200011)43:11<2437::aid-anr10>3.0.co;2-u
- PJ Clements, 2000, Skin thickness score as a predictor and correlate of outcome in systemic sclerosis: high-dose versus low-dose penicillamine trial., Arthritis Rheum, 43, 2445, 10.1002/1529-0131(200011)43:11<2445::AID-ANR11>3.0.CO;2-Q · doi:10.1002/1529-0131(200011)43:11<2445::aid-anr11>3.0.co;2-q
- AJ Barnett, 1988, A survival study of patients with scleroderma diagnosed over 30 years (1953–1983): the value of a simple cutaneous classification in the early stages of the disease., J Rheumatol, 15, 276
- VD Steen, 2001, Improvement in skin thickening in systemic sclerosis associated with improved survival., Arthritis Rheum, 44, 2828, 10.1002/1529-0131(200112)44:12<2828::AID-ART470>3.0.CO;2-U · doi:10.1002/1529-0131(200112)44:12<2828::aid-art470>3.0.co;2-u
- BM Kraling, 1996, Transplantation of systemic sclerosis (SSc) skin grafts into severe combined immunodeficient mice: increased presence of SSc leukocytes in SSc skin grafts and autoantibody production following autologous leukocyte transfer., Pathobiology, 64, 99, 10.1159/000164015 · doi:10.1159/000164015
- VV Yurovsky, 1994, Expansion of selected V delta 1+ gamma delta T cells in systemic sclerosis patients., J Immunol, 153, 881, 10.4049/jimmunol.153.2.881 · doi:10.4049/jimmunol.153.2.881
- WA Sewell, 1989, The T lymphocyte CD2 antigen–genetic and functional studies., Transplant Proc, 21, 41
- MJ Smyth, 1995, Cloning and characterization of a novel NK cell-specific serine protease gene and its functional 5′-flanking sequences., Immunogenetics, 42, 101, 10.1007/BF00178584 · doi:10.1007/bf00178584
- R Fleischmajer, 1977, [3H]Thymidine labeling of dermal endothelial cells in scleroderma., J Invest Dermatol, 69, 379, 10.1111/1523-1747.ep12510308 · doi:10.1111/1523-1747.ep12510308
- SA Douglas, 2000, Differential vasoconstrictor activity of human urotensin-II in vascular tissue isolated from the rat, mouse, dog, pig, marmoset and cynomolgus monkey., Br J Pharmacol, 131, 1262, 10.1038/sj.bjp.0703690 · doi:10.1038/sj.bjp.0703690
- P Grieco, 2005, Urotensin-II receptor ligands. From agonist to antagonist activity., J Med Chem, 48, 7290, 10.1021/jm058043j · doi:10.1021/jm058043j
- AA Margolin, 2005, CGHAnalyzer: a stand-alone software package for cancer genome analysis using array-based DNA copy number data., Bioinformatics, 21, 3308, 10.1093/bioinformatics/bti500 · doi:10.1093/bioinformatics/bti500
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Understanding fibrosis in systemic sclerosis: shifting paradigms, emerging opportunities
Bhattacharyya, S., Wei, J., & Varga, J. (2012) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2011.149 · Cited by 381
Chakraborty, D., Šumová, B., Mallano, T., Chen, C.-W., Distler, A., Bergmann, C., Ludolph, I., Horch, R. E., Gelse, K., Ramming, A., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01236-6 · Cited by 356
Marangoni, R. G., Korman, B. D., Wei, J., Wood, T. A., Graham, L. V., Whitfield, M. L., Scherer, P. E., Tourtellotte, W. G., & Varga, J. (2015) · Arthritis & Rheumatology · doi:10.1002/art.38990 · Cited by 337
Self-citationThe Pathogenesis of Systemic Sclerosis
Katsumoto, T. R., Whitfield, M. L., & Connolly, M. K. (2011) · Annual Review of Pathology: Mechanisms of Disease · doi:10.1146/annurev-pathol-011110-130312 · Cited by 313
Self-citationTransforming growth factor β as a therapeutic target in systemic sclerosis
Varga, J., & Pasche, B. (2009) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2009.26 · Cited by 295
Metadata
- Authors
- Milano, A., Pendergrass, S. A., Sargent, J. L., George, L. K., McCalmont, T. H., Connolly, M. K., & Whitfield, M. L.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-07-16
- Volume
- 3
- Issue
- 7
- Pages
- e2696
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2024866056
- Cited by
- 386
- References
- 80
- FWCI
- 12.64
- Type
- journal article
- Language
- en
- Update notices
- correction (2008-10-08)
- Open access
- gold
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