Transcriptomic and Network Analysis of Minor Salivary Glands of Patients With Primary Sjögren's Syndrome

Abstract

Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease characterized primarily by immune-mediated destruction of exocrine tissues, such as those of the salivary and lacrimal glands, resulting in the loss of saliva and tear production, respectively. This disease predominantly affects middle-aged women, often in an insidious manner with the accumulation of subtle changes in glandular function occurring over many years. Patients commonly suffer from pSS symptoms for years before receiving a diagnosis. Currently, there is no effective cure for pSS and treatment options and targeted therapy approaches are limited due to a lack of our overall understanding of the disease etiology and its underlying pathology. To better elucidate the underlying molecular nature of this disease, we have performed RNA-sequencing to generate a comprehensive global gene expression profile of minor salivary glands from an ethnically diverse cohort of patients with pSS. Gene expression analysis has identified a number of pathways and networks that are relevant in pSS pathogenesis. Moreover, our detailed integrative analysis has revealed a primary Sjögren's syndrome molecular signature that may represent important players acting as potential drivers of this disease. Finally, we have established that the global transcriptomic changes in pSS are likely to be attributed not only to various immune cell types within the salivary gland but also epithelial cells which are likely playing a contributing role. Overall, our comprehensive studies provide a database-enriched framework and resource for the identification and examination of key pathways, mediators, and new biomarkers important in the pathogenesis of this disease with the long-term goals of facilitating earlier diagnosis of pSS and to mitigate or abrogate the progression of this debilitating disease.

Keywords: RNA-sequencing; Sjögren’s syndrome; bioinformatics; gene expression; salivary gland.

Figure 1

Comprehensive Transcriptomic Analysis of Minor Salivary Glands of Patients with Sjögren’s Syndrome. (A) Plot demonstrates principal component analysis (PCA) coordinates for each minor salivary gland for eight non-pSS controls and nine pSS patient samples. Blue and red circles represent pSS and non-pSS, respectively. (B) Heatmap visualization of the top 100 differentially expressed genes (DEGs) in minor salivary glands of non-SS controls and pSS patient samples. Network visualization of enriched biological processes of the top 100 upregulated (C) and top 100 downregulated (D) DEGs in pSS.

Figure 2

Integrated Analysis Reveals a pSS Molecular Signature. (A) Venn diagram displaying the overlay of up-regulated genes identified in the RNA-seq dataset described here (Oyelakin et al.) and Min et al. (50) and Liu et al. (49) datasets. (B) Heatmap visualization of the 80 common upregulated genes identified in panel A. (C) Bar plot highlights biological processes enriched in the 80 common upregulated genes identified in panel A above.

Figure 3

Cell and Tissue Specific Distribution of Differentially Expressed Genes in pSS. Boxplot displaying the distribution of expression of upregulated (A) and downregulated (B) genes in pSS patient glands in various tissues and cell types generated by the Human Protein Atlas (HPA) project.

Figure 4

Cell Type Enrichment Analysis in pSS. Heatmap visualization of gene set enrichment scores by cell type. Scores were normalized across rows. The stroma and immune scores are displayed at the top of the heatmap. Cell types were assigned by the xCell algorithm based on the estimated enrichment of cell type signature genes in each bulk RNA-seq sample.

Figure 5

Gene Networks and Canonical Pathways Generated by Ingenuity Pathway Analysis (IPA). (A) Top 21 significantly affected canonical pathways for DEGs identified in our dataset based on IPA. Bars denote the different pathways based on Z-scores (2.0 < Z score < −2.0). Orange color indicates pathway activation, while blue denotes suppression. (B–D) Gene network analysis using IPA for pathways selected from panel A above. Solid lines denote high correlation between members of a defined cluster and dotted lines denote weak inter-cluster correlation.