Executive Summary : | Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a break in humoral immune tolerance and the expansion of autoreactive B cell clones. This leads to the production of autoantibodies against a broad spectrum of self-antigens, including nuclear ANAs, cytoplasmic, and membrane antigens. The underlying immune pathogenesis of SLE has been established to have significant contributions from plasmacytoid dendritic cell-derived type I interferons. Machine learning approaches are increasingly being explored to understand the heterogeneity of the pathogenetic network and stratify patients accordingly. The project aims to address this by mapping clinical heterogeneity in terms of autoantibody profiles and clinical manifestations, followed by a comprehensive multi-omics immuno-phenotyping study and a deep learning-based integrated analysis. The operative teams consist of clinical and basic immunologists and rheumatologists from both countries to achieve need-driven advancements that can be translated to global and country-specific clinical rheumatology practice. The project's collaboration with RL and DG is motivated by the potential to comprehensively address the intriguing questions about the pathogenesis of SLE, by gleaning invaluable insights from characterizing the prototypical systemic autoimmune disease in two cohorts of patients with discreet ethnic and geo-social attributes. |
Co-PI: | Prof. Parasar Ghosh, Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal (700020), Prof. Sanghamitra Bandyopadhyay, Indian Statistical Institute, Kolkata, West Bengal (700108) |