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Gene-expression profiling in rheumatic disease: tools and therapeutic potential

Abstract

Gene-expression profiling is a powerful tool for the discovery of molecular fingerprints that underlie human disease. Microarray technologies allow the analysis of messenger RNA transcript levels for every gene in the genome. However, gene-expression profiling is best viewed as part of a pipeline that extends from sample collection through clinical application. Key genes and pathways identified by microarray profiling should be validated in independent sample sets and with alternative technologies. Analysis of relevant signaling pathways at the protein level is an important step towards understanding the functional consequences of aberrant gene expression. Peripheral blood is a convenient and rich source of potential biomarkers, but surveying purified cell populations and target tissues can also enhance our understanding of disease states. In rheumatic disease, probing the transcriptome of circulating immune cells has shed light on mechanisms underlying the pathogenesis of complex diseases, such as systemic lupus erythematosus. As these discoveries advance through the pipeline, a variety of clinical applications are on the horizon, including the use of molecular fingerprints to aid in diagnosis and prognosis, improved use of existing therapies, and the development of drugs that target relevant genes and pathways.

Key Points

  • Gene-expression profiling is part of a pipeline that has led to identification of promising biomarkers in rheumatology

  • Sample collection and clinical end points must be considered at an early stage to ensure that high-quality samples and appropriate clinical data are collected

  • Genes and pathways identified by microarray analysis should be validated in independent sample collections and by alternative methods

  • Key genes and pathways identified in gene-expression studies can be examined at the protein level using a variety of multiplexed platforms

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Figure 1: Translation of discovery-based gene-expression microarray data into clinically useful tools.
Figure 2: Principles of RNA-sequencing-based gene-expression analysis.

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References

  1. Baechler, E. C. et al. Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation. Genes Immun. 5, 347–353 (2004).

    Article  CAS  PubMed  Google Scholar 

  2. Liu, J., Walter, E., Stenger, D. & Thach, D. Effects of globin mRNA reduction methods on gene expression profiles from whole blood. J. Mol. Diagn. 8, 551–558 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hashimoto, A. et al. Laser-mediated microdissection for analysis of gene expression in synovial tissue. Mod. Rheumatol. 17, 185–190 (2007).

    Article  CAS  PubMed  Google Scholar 

  4. Hoffmann, M. et al. Robust computational reconstitution—a new method for the comparative analysis of gene expression in tissues and isolated cell fractions. BMC Bioinformatics 7, 369 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Judex, M. et al. Laser-mediated microdissection facilitates analysis of area-specific gene expression in rheumatoid synovium. Arthritis Rheum. 48, 97–102 (2003).

    Article  CAS  PubMed  Google Scholar 

  6. Tsubaki, T. et al. Characterization of histopathology and gene-expression profiles of synovitis in early rheumatoid arthritis using targeted biopsy specimens. Arthritis Res. Ther. 7, R825–R836 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ueda, H. et al. Association of the T-cell regulatory gene CTLA4 with susceptibility to autoimmune disease. Nature 423, 506–511 (2003).

    Article  CAS  PubMed  Google Scholar 

  8. Haas, C. S. et al. Identification of genes modulated in rheumatoid arthritis using complementary DNA microarray analysis of lymphoblastoid B cell lines from disease-discordant monozygotic twins. Arthritis Rheum. 54, 2047–2060 (2006).

    Article  CAS  PubMed  Google Scholar 

  9. Baumann, S. et al. Standardized approach to proteome profiling of human serum based on magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Clin. Chem. 51, 973–980 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Illei, G. G., Tackey, E., Lapteva, L. & Lipsky, P. E. Biomarkers in systemic lupus erythematosus: II. Markers of disease activity. Arthritis Rheum. 50, 2048–2065 (2004).

    Article  CAS  PubMed  Google Scholar 

  11. Brown, P. O. & Botstein, D. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21, 33–37 (1999).

    Article  CAS  PubMed  Google Scholar 

  12. Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

    Article  CAS  PubMed  Google Scholar 

  13. Van Gelder, R. N. et al. Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc. Natl Acad. Sci. USA 87, 1663–1667 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wang, E., Miller, L. D., Ohnmacht, G. A., Liu, E. T. & Marincola, F. M. High-fidelity mRNA amplification for gene profiling. Nat. Biotechnol. 18, 457–459 (2000).

    Article  CAS  PubMed  Google Scholar 

  15. Stirewalt, D. L. et al. Single-stranded linear amplification protocol results in reproducible and reliable microarray data from nanogram amounts of starting RNA. Genomics 83, 321–331 (2004).

    Article  CAS  PubMed  Google Scholar 

  16. Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Pan, Q., Shai, O., Lee, L. J., Frey, B. J. & Blencowe, B. J. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 40, 1413–1415 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Simon, S. A. et al. Short-read sequencing technologies for transcriptional analyses. Annu. Rev. Plant Biol. [doi:10.1146/annurev.arplant.043008.092032] (2009).

  20. 't Hoen, P. A. et al. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Res. 36, e141 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ioannidis, J. P. Microarrays and molecular research: noise discovery? Lancet 365, 454–455 (2005).

    Article  PubMed  Google Scholar 

  22. Simon, R., Radmacher, M. D. & Dobbin, K. Design of studies using DNA microarrays. Genet. Epidemiol. 23, 21–36 (2002).

    Article  PubMed  Google Scholar 

  23. Liang, Y. & Kelemen, A. Associating phenotypes with molecular events: recent statistical advances and challenges underpinning microarray experiments. Funct. Integr. Genomics 6, 1–13 (2006).

    Article  CAS  PubMed  Google Scholar 

  24. Arya, M. et al. Basic principles of real-time quantitative PCR. Expert Rev. Mol. Diagn. 5, 209–219 (2005).

    Article  CAS  PubMed  Google Scholar 

  25. Huggett, J., Dheda, K., Bustin, S. & Zumla, A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 6, 279–284 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. May, M. Life science technologies: qPCR—making older technology new again. Science 321, 1696–1699 (2008).

    Article  Google Scholar 

  27. Provenzano, M. & Mocellin, S. Complementary techniques: validation of gene expression data by quantitative real time PCR. Adv. Exp. Med. Biol. 593, 66–73 (2007).

    Article  PubMed  Google Scholar 

  28. VanGuilder, H. D., Vrana, K. E. & Freeman, W. M. Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques 44, 619–626 (2008).

    Article  CAS  PubMed  Google Scholar 

  29. Marras, S. A., Tyagi, S. & Kramer, F. R. Real-time assays with molecular beacons and other fluorescent nucleic acid hybridization probes. Clin. Chim. Acta 363, 48–60 (2006).

    Article  CAS  PubMed  Google Scholar 

  30. Solinas, A. et al. Duplex Scorpion primers in SNP analysis and FRET applications. Nucleic Acids Res. 29, E96 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lyng, M. B. et al. Intratumor genetic heterogeneity of breast carcinomas as determined by fine needle aspiration and TaqMan low density array. Cell. Oncol. 29, 361–372 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Yoshida, T., Jiang, F., Honjo, T. & Okazaki, T. PD-1 deficiency reveals various tissue-specific autoimmunity by H-2b and dose-dependent requirement of H-2g7 for diabetes in NOD mice. Proc. Natl Acad. Sci. USA 105, 3533–3538 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ismail, A. A., Walker, P. L., Cawood, M. L. & Barth, J. H. Interference in immunoassay is an underestimated problem. Ann. Clin. Biochem. 39, 366–373 (2002).

    Article  CAS  PubMed  Google Scholar 

  34. Balboni, I. et al. Multiplexed protein array platforms for analysis of autoimmune diseases. Annu. Rev. Immunol. 24, 391–418 (2006).

    Article  CAS  PubMed  Google Scholar 

  35. Fathman, C. G., Soares, L., Chan, S. M. & Utz, P. J. An array of possibilities for the study of autoimmunity. Nature 435, 605–611 (2005).

    Article  CAS  PubMed  Google Scholar 

  36. Slamon, D. J. et al. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235, 177–182 (1987).

    Article  CAS  PubMed  Google Scholar 

  37. Slamon, D. J. et al. Studies of the HER-2/neu proto-oncogene in human breast and ovarian cancer. Science 244, 707–712 (1989).

    Article  CAS  PubMed  Google Scholar 

  38. Nuyten, D. S. & van de Vijver, M. J. Using microarray analysis as a prognostic and predictive tool in oncology: focus on breast cancer and normal tissue toxicity. Semin. Radiat. Oncol. 18, 105–114 (2008).

    Article  PubMed  Google Scholar 

  39. Bomprezzi, R. et al. Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to disease. Hum. Mol. Genet. 12, 2191–2199 (2003).

    Article  CAS  PubMed  Google Scholar 

  40. Sturzebecher, S. et al. Expression profiling identifies responder and non-responder phenotypes to interferon-β in multiple sclerosis. Brain 126, 1419–1429 (2003).

    Article  CAS  PubMed  Google Scholar 

  41. Baechler, E. C. et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc. Natl Acad. Sci. USA 100, 2610–2615 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bennett, L. et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J. Exp. Med. 197, 711–723 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Maas, K. et al. Cutting edge: molecular portrait of human autoimmune disease. J. Immunol. 169, 5–9 (2002).

    Article  CAS  PubMed  Google Scholar 

  44. Stoeckman, A. K. et al. A distinct inflammatory gene expression profile in patients with psoriatic arthritis. Genes Immun. 7, 583–591 (2006).

    Article  CAS  PubMed  Google Scholar 

  45. Batliwalla, F. M. et al. Microarray analyses of peripheral blood cells identifies unique gene expression signature in psoriatic arthritis. Mol. Med. 11, 21–29 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Batliwalla, F. M. et al. Peripheral blood gene expression profiling in rheumatoid arthritis. Genes Immun. 6, 388–397 (2005).

    Article  CAS  PubMed  Google Scholar 

  47. van der Pouw Kraan, T. C. et al. Rheumatoid arthritis subtypes identified by genomic profiling of peripheral blood cells: assignment of a type I interferon signature in a subpopulation of patients. Ann. Rheum. Dis. 66, 1008–1014 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Koczan, D. et al. Gene expression profiling of peripheral blood mononuclear leukocytes from psoriasis patients identifies new immune regulatory molecules. Eur. J. Dermatol. 15, 251–257 (2005).

    CAS  PubMed  Google Scholar 

  49. Petty, R. E. et al. International League of Associations for Rheumatology classification of juvenile idiopathic arthritis: second revision, Edmonton, 2001. J. Rheumatol. 31, 390–392 (2004).

    PubMed  Google Scholar 

  50. Pascual, V., Allantaz, F., Arce, E., Punaro, M. & Banchereau, J. Role of interleukin-1 (IL-1) in the pathogenesis of systemic onset juvenile idiopathic arthritis and clinical response to IL-1 blockade. J. Exp. Med. 201, 1479–1486 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Adams, A. & Lehman, T. J. Update on the pathogenesis and treatment of systemic onset juvenile rheumatoid arthritis. Curr. Opin. Rheumatol. 17, 612–616 (2005).

    Article  CAS  PubMed  Google Scholar 

  52. Buch, M. H. et al. Lack of response to anakinra in rheumatoid arthritis following failure of tumor necrosis factor alpha blockade. Arthritis Rheum. 50, 725–728 (2004).

    Article  CAS  PubMed  Google Scholar 

  53. Genovese, M. C. et al. Combination therapy with etanercept and anakinra in the treatment of patients with rheumatoid arthritis who have been treated unsuccessfully with methotrexate. Arthritis Rheum. 50, 1412–1419 (2004).

    Article  CAS  PubMed  Google Scholar 

  54. Kirou, K. A. et al. Coordinate overexpression of interferon-α-induced genes in systemic lupus erythematosus. Arthritis Rheum. 50, 3958–3967 (2004).

    Article  CAS  PubMed  Google Scholar 

  55. Baechler, E. C., Gregersen, P. K. & Behrens, T. W. The emerging role of interferon in human systemic lupus erythematosus. Curr. Opin. Immunol. 16, 801–807 (2004).

    Article  CAS  PubMed  Google Scholar 

  56. Kirou, K. A. et al. Activation of the interferon-α pathway identifies a subgroup of systemic lupus erythematosus patients with distinct serologic features and active disease. Arthritis Rheum. 52, 1491–1503 (2005).

    Article  CAS  PubMed  Google Scholar 

  57. Bauer, J. W. et al. Elevated serum levels of interferon-regulated chemokines are biomarkers for active human systemic lupus erythematosus. PLoS Med. 3, e491 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Banchereau, J. & Pascual, V. Type I interferon in systemic lupus erythematosus and other autoimmune diseases. Immunity 25, 383–392 (2006).

    Article  CAS  PubMed  Google Scholar 

  59. Blomberg, S. et al. Presence of cutaneous interferon-α producing cells in patients with systemic lupus erythematosus. Lupus 10, 484–490 (2001).

    Article  CAS  PubMed  Google Scholar 

  60. Fah, J., Pavlovic, J. & Burg, G. Expression of MxA protein in inflammatory dermatoses. J. Histochem. Cytochem. 43, 47–52 (1995).

    Article  CAS  PubMed  Google Scholar 

  61. Peterson, K. S. et al. Characterization of heterogeneity in the molecular pathogenesis of lupus nephritis from transcriptional profiles of laser-captured glomeruli. J. Clin. Invest. 113, 1722–1733 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Farkas, L., Beiske, K., Lund-Johansen, F., Brandtzaeg, P. & Jahnsen, F. L. Plasmacytoid dendritic cells (natural interferon-α/β-producing cells) accumulate in cutaneous lupus erythematosus lesions. Am. J. Pathol. 159, 237–243 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ronnblom, L. & Alm, G. V. A pivotal role for the natural interferon α-producing cells (plasmacytoid dendritic cells) in the pathogenesis of lupus. J. Exp. Med. 194, F59–F63 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Diebold, S. S., Kaisho, T., Hemmi, H., Akira, S. & Reis e Sousa, C. Innate antiviral responses by means of TLR7-mediated recognition of single-stranded RNA. Science 303, 1529–1531 (2004).

    Article  CAS  PubMed  Google Scholar 

  65. Heil, F. et al. Species-specific recognition of single-stranded RNA via Toll-like receptor 7 and 8. Science 303, 1526–1529 (2004).

    Article  CAS  PubMed  Google Scholar 

  66. Hemmi, H. et al. A Toll-like receptor recognizes bacterial DNA. Nature 408, 740–745 (2000).

    Article  CAS  PubMed  Google Scholar 

  67. Bave, U., Alm, G. V. & Ronnblom, L. The combination of apoptotic U937 cells and lupus IgG is a potent IFN-α inducer. J. Immunol. 165, 3519–3526 (2000).

    Article  CAS  PubMed  Google Scholar 

  68. Lovgren, T., Eloranta, M. L., Bave, U., Alm, G. V. & Ronnblom, L. Induction of interferon-α production in plasmacytoid dendritic cells by immune complexes containing nucleic acid released by necrotic or late apoptotic cells and lupus IgG. Arthritis Rheum. 50, 1861–1872 (2004).

    Article  CAS  PubMed  Google Scholar 

  69. Davies, K. A., Peters, A. M., Beynon, H. L. & Walport, M. J. Immune complex processing in patients with systemic lupus erythematosus. In vivo imaging and clearance studies. J. Clin. Invest. 90, 2075–2083 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Akira, S. & Takeda, K. Toll-like receptor signalling. Nat. Rev. Immunol. 4, 499–511 (2004).

    Article  CAS  PubMed  Google Scholar 

  71. Pascual, V., Farkas, L. & Banchereau, J. Systemic lupus erythematosus: all roads lead to type I interferons. Curr. Opin. Immunol. 18, 676–682 (2006).

    Article  CAS  PubMed  Google Scholar 

  72. Baechler, E. C. et al. An interferon signature in the peripheral blood of dermatomyositis patients is associated with disease activity. Mol. Med. 13, 59–68 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Greenberg, S. A. et al. Interferon-α/β-mediated innate immune mechanisms in dermatomyositis. Ann. Neurol. 57, 664–678 (2005).

    Article  CAS  PubMed  Google Scholar 

  74. Lopez de Padilla, C. M. et al. Plasmacytoid dendritic cells in inflamed muscle of patients with juvenile dermatomyositis. Arthritis Rheum. 56, 1658–1668 (2007).

    Article  PubMed  Google Scholar 

  75. Nomura, I. et al. Distinct patterns of gene expression in the skin lesions of atopic dermatitis and psoriasis: a gene microarray analysis. J. Allergy Clin. Immunol. 112, 1195–1202 (2003).

    Article  CAS  PubMed  Google Scholar 

  76. Oestreicher, J. L. et al. Molecular classification of psoriasis disease-associated genes through pharmacogenomic expression profiling. Pharmacogenomics J. 1, 272–287 (2001).

    Article  CAS  PubMed  Google Scholar 

  77. Coelho, L. F., de Oliveira, J. G. & Kroon, E. G. Interferons and scleroderma—a new clue to understanding the pathogenesis of scleroderma? Immunol. Lett. 118, 110–115 (2008).

    Article  CAS  PubMed  Google Scholar 

  78. Wildenberg, M. E., van Helden-Meeuwsen, C. G., van de Merwe, J. P., Drexhage, H. A. & Versnel, M. A. Systemic increase in type I interferon activity in Sjögren's syndrome: a putative role for plasmacytoid dendritic cells. Eur. J. Immunol. 38, 2024–2033 (2008).

    Article  CAS  PubMed  Google Scholar 

  79. Wallace, D. et al. MEDI-545, an anti-interferon-α monoclonal antibody, shows evidence of clinical activity in systemic lupus erythematosus [Abstract 1315]. Program and abstracts of the American College of Rheumatology 71st Annual Meeting; 6–11 November, 2007; Boston, Massachusetts.

  80. Lettre, G. & Rioux, J. D. Autoimmune diseases: insights from genome-wide association studies. Hum. Mol. Genet. 17, R116–R121 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Merrill, J. T., Erkan, D. & Buyon, J. P. Challenges in bringing the bench to bedside in drug development for SLE. Nat. Rev. Drug Discov. 3, 1036–1046 (2004).

    Article  CAS  PubMed  Google Scholar 

  82. Shirota, Y., Illei, G. G. & Nikolov, N. P. Biologic treatments for systemic rheumatic diseases. Oral Dis. 14, 206–216 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Mease, P. J. B cell-targeted therapy in autoimmune disease: rationale, mechanisms, and clinical application. J. Rheumatol. 35, 1245–1255 (2008).

    CAS  PubMed  Google Scholar 

  84. Calabrese, L. H., Molloy, E. S., Huang, D. & Ransohoff, R. M. Progressive multifocal leukoencephalopathy in rheumatic diseases: evolving clinical and pathologic patterns of disease. Arthritis Rheum. 56, 2116–2128 (2007).

    Article  PubMed  Google Scholar 

  85. Filipowicz, W., Bhattacharyya, S. N. & Sonenberg, N. Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat. Rev. Genet. 9, 102–114 (2008).

    Article  CAS  PubMed  Google Scholar 

  86. Dai, Y. et al. Microarray analysis of microRNA expression in peripheral blood cells of systemic lupus erythematosus patients. Lupus 16, 939–946 (2007).

    Article  CAS  PubMed  Google Scholar 

  87. Engvall, E., Jonsson, K. & Perlmann, P. Enzyme-linked immunosorbent assay. II. Quantitative assay of protein antigen, immunoglobulin G, by means of enzyme-labelled antigen and antibody-coated tubes. Biochim. Biophys. Acta 251, 427–434 (1971).

    Article  CAS  PubMed  Google Scholar 

  88. Engvall, E. & Perlman, P. Enzyme-linked immunosorbent assay (ELISA). Quantitative assay of immunoglobulin G. Immunochemistry 8, 871–874 (1971).

    Article  CAS  PubMed  Google Scholar 

  89. Perlee, L. et al. Development and standardization of multiplexed antibody microarrays for use in quantitative proteomics. Proteome Sci. 2, 9 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Fulton, R. J., McDade, R. L., Smith, P. L., Kienker, L. J. & Kettman, J. R. Jr Advanced multiplexed analysis with the FlowMetrix system. Clin. Chem. 43, 1749–1756 (1997).

    CAS  PubMed  Google Scholar 

  91. Kingsmore, S. F. Multiplexed protein measurement: technologies and applications of protein and antibody arrays. Nat. Rev. Drug Discov. 5, 310–320 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Mendoza, L. G. et al. High-throughput microarray-based enzyme-linked immunosorbent assay (ELISA). Biotechniques 27, 778–788 (1999).

    Article  CAS  PubMed  Google Scholar 

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Correspondence to Emily C. Baechler.

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Emily C. Baechler is a co-inventor on the following patent: Behrens, T., Baechler; E. C., Gregersen, P. K. Methods for diagnosing severe systemic lupus erythematosus. US Patent 7,118,865 (2006).

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Bauer, J., Bilgic, H. & Baechler, E. Gene-expression profiling in rheumatic disease: tools and therapeutic potential. Nat Rev Rheumatol 5, 257–265 (2009). https://doi.org/10.1038/nrrheum.2009.50

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