Methods: Sjögren's syndrome is a tissue-specific autoimmune disease that affects exocrine tissues, especially salivary glands and lacrimal glands. Sjögren's syndrome is one of the most common autoimmune disorders in the US, with an estimated prevalence of 2-4 million people. Despite a sustained effort by many investigators several important gaps in our knowledge about Sjögren's syndrome remain. To address these knowledge gaps, we have built a Sjögren's Syndrome Knowledge Base (SSKB) that integrates existing knowledge in the field. The text mining program EBIMed was used to query the PubMed database with the search term "Sjogren's Syndrome" restricted to “MeshHeadingsList”.
Results: The initial search resulted in a selection of 7733 abstracts and 1293 potential genes/proteins. The abstracts were manually evaluated to remove duplicates and false-positives. In the case of older publications, where gene names were not readily identifiable, gene names were assigned based on in-depth evaluation of the protein name context and available gene data in public databases, including Entrez and Uniprot. Using Webgestalt, 75 KEGG pathways were identified in this gene subset. A similar analysis of gene expression in parotid glands from Sjögren's syndrome patients revealed a significant overlap with the existing data in the SSKB.
Conclusions: Current gene expression data reflect the existing knowledge of genes/proteins that have been reported to play a role in Sjögren's syndrome. Prominent biological pathways include B-cell and T-cell maturation, inflammation and apoptosis. This research was supported by NIH grant 1R01DE019255-01 from the National Institute for Dental and Craniofacial Research.