Arthritis & Rheumatism, Volume 62,
November 2010 Abstract Supplement
Abstracts of the American College of
Rheumatology/Association of Rheumatology Health Professionals
Annual Scientific Meeting
Atlanta, Georgia November 6-11, 2010.
A Systems Biology Approach to Understanding SLE Complexity.
Jung1, John, Hossler1, Jennifer, Huang1, Youqun, Palmer1, Elise, Marin1, Elides, Sanford1, Tracy, Akhter2, Ehtisham
B cell involvement in SLE has been demonstrated as a significant contributing determinant to disease pathogenesis. However, phenotypic heterogeneity and physiological importance of B cell subsets remain elusive and pose a challenge to understanding the complex mechanisms resulting in autoimmunity. Therefore, utilizing 3 different 12-color flow cytometry panels we have captured a comprehensive high-resolution footprint of the B cell profile in SLE in order to systematically explore the diverse human B cell repertoire and identify biomarkers of disease activity and prognosis.
B-cells from Healthy Controls (n=26) and SLE patients (n=127) were analyzed by multi-color flow cytometry for expression of anchor markers that identify larger parental B cell populations. The individual panels also included subset specific markers to further define parental populations: Memory; Transitional and Naive; and Plasma B cells. Multivariate methods were used to seek natural divisions based on the B cell profiles, and to relate them to various clinical parameters. SLE patients met ACR criteria for the classification of SLE, and were sub-categorized based on primary clinical manifestation. Disease activity and flares were measured by SELENA-SLEDAI and physician global assessment.
Preliminary analyses show SLE B cell profiles exhibited a broader spread across different subsets compared to Healthy Control (HC). However, after sub-categorizing SLE patients based on primary clinical manifestations, the SLE sub-categories were revealed to have distinct B cell profiles. Overall, SLE patients had higher transitional populations and lower true naive B cells than HC (p<0.05). Nephritis, musculoskeletal/skin, and flaring patients exhibited a CD27- memory B cell expansion (p<0.05) with expression of CD95+ (p<0.05) and CD21- (p<0.05), markers of putative effector memory B cells, compared to HC. Nephritis patients had a unique memory CD27- B cell subset expressing significantly higher B220+ (p<0.05) and CD24- (p<0.0005). Musculoskeletal/skin patients exhibit CD27- memory that expressed CXCR3+ and high CD27+ memory with CD95+ (p<0.005) when compared to HC. Overall, Spearman correlation shows SLE DAI scores correlated with CD27- Memory with phenotypes CD95+(r=0.46, p<0.005), CD21-(r=0.44, p<0.005), and CD24- (r=0.44, p<0.005). SLE DAI also correlated with plasmablast counts (r=0.35,p<0.05).
These results indicate that unique B-cell signatures cluster with distinct clinical parameters, indicating a spectrum of disease states in SLE. Moreover, the exploration of multiple B cell subsets by a multi-colored flow-cytometry approach suggests that B cell profiles may serve as biomarkers of disease phenotype, activity, and treatment response. These profiles will be invaluable to understand disease heterogeneity and possibly its genetic basis and should prove of great help in the design of future therapeutic studies, especially with B cell targeting agents. Ongoing studies are exploring longitudinal changes in B cell phenotypes and correlation with T cell abnormalities to create an even more comprehensive picture of SLE disease pathogenesis.
To cite this abstract, please use the following information:
Jung, John, Hossler, Jennifer, Huang, Youqun, Palmer, Elise, Marin, Elides, Sanford, Tracy, et al; A Systems Biology Approach to Understanding SLE Complexity. [abstract]. Arthritis Rheum 2010;62 Suppl 10 :733