THU0342 SYSTEMIC SCLEROSIS IS A DISEASE OF A PREMATURELY SENESCENT, INFLAMMATORY AND ACTIVATED IMMUNOME. (June 2019)

• Record Type: Journal Article
• Authors: Paleja, Bhairav; Low, Andrea; Kumar, Pavanish; Saidin, Suzan; Lajam, Ahmad; Hazirah, Sharifah Nur; Chua, Camillus; Lai, Liyun; Albani, Salvatore
• Is Part Of: Annals of the rheumatic diseases. Volume 78(2019)Supplement 2
• Page Start: 452
• Page End: 453
• Publication Date: 2019-06
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2019-eular.5253
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Systemic sclerosis (SSc) is an autoimmune disease characterised by excessive fibrosis of skin and internal organs, and vascular dysfunction (1). Association of T and B cell subsets have been reported in SSc, however there is lack of systematic studies of functional relations between immune cell subsets in this disease (2, 3, 4). This lack of mechanistic knowledge hampers targeted intervention. Objectives: In the current study we ought to determine differential immune cell composition and their interactions in peripheral blood of SSc patients and its impact on disease severity and progression. Methods: Mononuclear cells from blood of SSc patients (n=20) and healthy controls (n=10) were analysed by mass cytometry using a 36 marker (cell-surface and intracellular) panel to aid in identification of major PBMC lineages including T cells, B cells, monocytes and NK cells and their subsets. Transcriptome analysis (m-RNA sequencing) was performed on sorted T and B cell subsets. Unsupervised clustering of mass cytometry data was performed using in-house developed analysis software MARVIS. This software combines dimension reduction and clustering steps to identify all possible cellular subsets. Further, custom R scripts helped in identifying nodes that were differentially expressed between the study groups and also phenotype of these nodes. Results: Unsupervised clustering analysis revealed significant differences in the frequencies of T and B cell subsets in patients. Correlation network analysis highlighted an overall dysregulated immune architecture coupled with domination of inflammatory senescent T cell modules in SSc patients. Transcriptome analysis of sorted immune cells revealed an activated phenotype of CD4 and MAIT cells in patients, accompanied with increased expression of inhibitory molecules, reminiscent of phenotype exhibited by functionally adapted, exhausted T cells in response to chronic stimulation. Conclusion: Overall this study provides an in depth analysis of systemic immunome in SSc, highlighting role of inflammation and chronic stimulation mediated "premature senescence" of immune cells, with implications to delineate mechanism of pathogenesis and identify diagnostic/therapeutic targets. References: [1] Denton, C. P. and D. Khanna (2017). "Systemic sclerosis." Lancet 390(10103): 1685-1699. [2] Fuschiotti, P. (2017). "Current perspectives on the role of CD8+ T cells in systemic sclerosis." Immunol Lett. In Press [3] Liu, M., et al. (2016). "New insights into CD4(+) T cell abnormalities in systemic sclerosis." Cytokine Growth Factor Rev 28: 31-36. [4] Sanges, S., et al. (2017). "Role of B cells in the pathogenesis of systemic sclerosis." Rev Med Interne 38(2): 113-124. Disclosure of Interests: None declared