Abstract: Primary Sjögren’s syndrome (pSS) is an autoimmune disease characterized by lymphoid infiltration of exocrine glands leading to dryness of the mucosal surfaces and by the production of various autoantibodies. The pathophysiology of pSS remains elusive and no treatment with demonstrated efficacy is available yet.
To better understand the system biology underlying pSS heterogeneity, we aimed at identifying Consensus Gene Modules (CGMs) summarizing the high-dimensional transcriptomic data of whole blood samples in pSS patients. We performed an unsupervised classification and identified 11 CGMs. We interpreted and annotated each of these CGMs as corresponding to cell type abundances or biological functions by using gene set enrichment analyses and transcriptomic profiles of sorted blood subsets. Correlation with independently measured cytokine levels and cell type abundances by flow cytometry confirmed these annotations.
By measuring the average expression of the CGMs on samples from clinical trials, we confirmed previously described relationships between the presence of autoantibodies, activation of the type I interferon pathway and an increased frequency of monocytes. Furthermore, we will study whether the expression of the CGMs can predict response to treatments. We believe that these CGMs will facilitate the interpretation of whole blood transcriptomic data of pSS patients.

Keywords: Precision Medicine, Sjögren’s syndrome, Unsupervised learning, Integrated analysis.