Authors: Sehi Kim, Naomi Rapier-Sharman, Michael Told
Mentors: Brett Pickett
Insitution: Brigham Young University
Systemic Lupus Erythematosus is an autoimmune disease that produces autoantibodies affecting various body regions, including skin, joints, kidneys, brain, aerosol surfaces, blood vessels, etc., resulting in damaging organs and tissue. Patients commonly experience an elevated risk of bleeding or blood clotting, joint stiffness, pain, fatigue, and depression.
Our study involved the collection of RNA seq data of B cells of both SLE patients and healthy people from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Subsequently, we employed the Automated Reproducible MOdular Workflow for preprocessing and differential analysis of RNA-seq data (ARMOR) workflow. The differentially expressed genes identified by ARMOR were then analyzed using SPIA (Signaling Pathway Impact Analysis) algorithm to find the pathways associated with lupus. We further utilized the Pathways2Targets algorithm to predict potential lupus treatments based on known protein-drug interactions.
In our study on lupus patients, analysis using ARMOR, SPIA, and Pathways2Targets identified 10,000 differentially expressed genes and revealed their modulated pathways, providing insights into molecular cascades in lupus. Furthermore, we identified potential drug targets, finding the way for therapeutic interventions that ultimately led to the discovery of new drug treatments. We anticipate that our findings could be utilized for the benefit of lupus patients, further advancing personalized medicine strategies, holding promise for improving the quality of life for individuals grappling with this complex autoimmune disorder.