Peripheral blood gene expression profiling in Sjögren's syndrome

E. S. Emamian, J. M. Leon, C. J. Lessard, M. Grandits, E. C. Baechler, P. M. Gaffney, B. Segal, N. L. Rhodus, K. L. Moser

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218 Scopus citations


Sjögren's syndrome (SS) is a common chronic autoimmune disease characterized by lymphocytic infiltration of exocrine glands. The affected cases commonly present with oral and ocular dryness, which is thought to be the result of inflammatory cell-mediated gland dysfunction. To identify important molecular pathways involved in SS, we used high-density microarrays to define global gene expression profiles in the peripheral blood. We first analyzed 21 SS cases and 23 controls, and identified a prominent pattern of overexpressed genes that are inducible by interferons (IFNs). These results were confirmed by evaluation of a second independent data set of 17 SS cases and 22 controls. Additional inflammatory and immune-related pathways with altered expression patterns in SS cases included B- and T-cell receptor, insulin-like growth factor-1, granulocyte macrophage-colony stimulating factor, peroxisome proliferator-activated receptor-α/retinoid X receptor-α and PI3/AKT signaling. Exploration of these data for relationships to clinical features of disease showed that expression levels for most interferon-inducible genes were positively correlated with titers of anti-Ro/SSA (P<0.001) and anti-La/SSB (P < 0.001) autoantibodies. Diagnostic and therapeutic approaches targeting interferon-signaling pathway may prove most effective in the subset of SS cases that produce anti-Ro/SSA and anti-La/SSB autoantibodies. Our results strongly support innate and adaptive immune processes in the pathogenesis of SS, and provide numerous candidate disease markers for further study.

Original languageEnglish (US)
Pages (from-to)285-296
Number of pages12
JournalGenes and Immunity
Issue number4
StatePublished - 2009

Bibliographical note

Funding Information:
This study was funded by NIH NIAMS RO1 AR050782 and the Phileona Foundation (KLM). The authors are grateful for resources provided by the University of Minnesota Supercomputing Institute and the Affymetrix core. We also thank Carolyn M Meyer, Amber N Leiran, Liliana Tobon, Daniella Machado and Julie Ermer for their technical assistance, and Jennifer Lessard for assistance with graphics. Finally, we thank the study participants without whom this study would not be possible.


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