OBJECTIVE: Production of pathogenic autoantibodies by self-reactive plasma cells (PCs) is a hallmark of autoimmune diseases. We undertook this study to investigate the prevalence of PCs and their relationship to known pathogenic pathways to increase our understanding of the role of PCs in disease progression and treatment response. METHODS: We developed a sensitive gene expression-based method to overcome the challenges of measuring PCs using flow cytometry. Whole-genome microarray analysis of sorted cellular fractions identified a panel of genes, IGHA1, IGJ, IGKC, IGKV4-1, and TNFRSF17, expressed predominantly in PCs. The sensitivity of the PC signature score created from the combined expression levels of these genes was assessed through ex vivo experiments with sorted cells. This PC gene expression signature was used for monitoring changes in PC levels following anti-CD19 therapy, for evaluating the relationship between PCs and other autoimmune disease-related genes, and for estimating PC levels in affected blood and tissue from patients with multiple autoimmune diseases. RESULTS: The PC signature was highly sensitive and capable of detecting a change in as few as 360 PCs. The PC signature was reduced more than 90% in scleroderma patients following anti-CD19 treatment, and this reduction was highly correlated (r = 0.80) with inhibition of collagen gene expression. Evaluation of multiple autoimmune diseases revealed that 30-35% of lupus and rheumatoid arthritis patients had increased levels of PCs. CONCLUSION: This newly developed PC signature provides a robust and accurate method of measuring PC levels in the clinic. Our results highlight subsets of patients across multiple autoimmune diseases who may benefit from PC-depleting therapy.
OBJECTIVE: Production of pathogenic autoantibodies by self-reactive plasma cells (PCs) is a hallmark of autoimmune diseases. We undertook this study to investigate the prevalence of PCs and their relationship to known pathogenic pathways to increase our understanding of the role of PCs in disease progression and treatment response. METHODS: We developed a sensitive gene expression-based method to overcome the challenges of measuring PCs using flow cytometry. Whole-genome microarray analysis of sorted cellular fractions identified a panel of genes, IGHA1, IGJ, IGKC, IGKV4-1, and TNFRSF17, expressed predominantly in PCs. The sensitivity of the PC signature score created from the combined expression levels of these genes was assessed through ex vivo experiments with sorted cells. This PC gene expression signature was used for monitoring changes in PC levels following anti-CD19 therapy, for evaluating the relationship between PCs and other autoimmune disease-related genes, and for estimating PC levels in affected blood and tissue from patients with multiple autoimmune diseases. RESULTS: The PC signature was highly sensitive and capable of detecting a change in as few as 360 PCs. The PC signature was reduced more than 90% in sclerodermapatients following anti-CD19 treatment, and this reduction was highly correlated (r = 0.80) with inhibition of collagen gene expression. Evaluation of multiple autoimmune diseases revealed that 30-35% of lupus and rheumatoid arthritispatients had increased levels of PCs. CONCLUSION: This newly developed PC signature provides a robust and accurate method of measuring PC levels in the clinic. Our results highlight subsets of patients across multiple autoimmune diseases who may benefit from PC-depleting therapy.
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