The inflammatory signature in monocytes of Sjögren’s syndrome and systemic lupus erythematosus, revealed by the integrated Reactome and drug target analysis

Background The innate immune regulation, especially by the type I IFN signature in the CD14+ monocytes, is known to be critical in the pathogenesis of autoimmune Sjögren’s syndrome (SjS) and systemic lupus erythematosus (SLE). Objective Since patients with one condition can be overlapped with another, this study is to identify shared differentially expressed genes (DEGs) in SjS and SLE compared to healthy controls (HCs) and refine transcriptomic profiles with the integrated Reactome and gene-drug network analysis for an anti-inflammation therapy. Methods CD14+ monocytes were purified from whole blood of SjS and SLE patients (females, ages from 32 to 62) and subject to bulk RNA-sequencing, followed by data analyses for comparison with HC monocytes (females, ages 30 and 33). Functional categorizations, using Gene Ontology (GO) and the Reactome pathway analysis, were performed and DEGs associated with therapeutic drugs were identified from the Drug Repurposing Hub (DHUB) database. Results The GO analysis revealed that DEGs in the inflammatory response and the cellular response to cytokine were highly enriched in both conditions. A propensity toward M1 macrophage differentiation appears to be prominent in SjS while the Response to Virus was significant in SLE monocytes. Through the Reactome pathway analysis, DEGs in the IFN signaling and the cytokine signaling in immune system were most significantly enriched in both. Upregulation of NGF-induced transcription activity in SjS and the complement cascade activity in SLE were also noted. Multiple anti-inflammatory drugs, such as prostaglandin-endoperoxide synthase and angiotensin-I-converting- enzyme were associated with the DEGs in these conditions. Conclusions Taken together, our analysis indicates distinct inflammatory transcriptomic profiles shared in SjS and SLE monocytes. Comprehensive characterizations of the data from these conditions will ultimately allow differential diagnosis of each condition and identification of therapeutic targets.