Early breaches in B cell tolerance are central to type 1 diabetes progression in mouse and man. Conventional BCR transgenic mouse models (VH125.Tg NOD) reveal the power of B cell specificity to drive disease as APCs. However, in conventional fixed IgM models, comprehensive assessment of B cell development is limited. To provide more accurate insight into the developmental and functional fates of anti-insulin B cells, we generated a new NOD model (VH125SD.NOD) in which anti-insulin VDJH125 is targeted to the IgH chain locus to generate a small (1–2%) population of class switch–competent insulin-binding B cells. Tracking of this rare population in a polyclonal repertoire reveals that anti-insulin B cells are preferentially skewed into marginal zone and late transitional subsets known to have increased sensitivity to proinflammatory signals. Additionally, IL-10 production, characteristic of regulatory B cell subsets, is increased. In contrast to conventional models, class switch–competent anti-insulin B cells proliferate normally in response to mitogenic stimuli but remain functionally silent for insulin autoantibody production. Diabetes development is accelerated, which demonstrates the power of anti-insulin B cells to exacerbate disease without differentiation into Ab-forming or plasma cells. Autoreactive T cell responses in VH125SD.NOD mice are not restricted to insulin autoantigens, as evidenced by increased IFN-γ production to a broad array of diabetes-associated epitopes. Together, these results independently validate the pathogenic role of anti-insulin B cells in type 1 diabetes, underscore their diverse developmental fates, and demonstrate the pathologic potential of coupling a critical β cell specificity to predominantly proinflammatory Ag-presenting B cell subsets.
Anti-Insulin B Cells Are Poised for Antigen Presentation in Type 1 Diabetes
J. Felton,Damian Maseda,R. Bonami,C. Hulbert,James W. Thomas
Published 2018 in Journal of Immunology
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- Publication year
2018
- Venue
Journal of Immunology
- Publication date
2018-06-27
- Fields of study
Biology, Medicine
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- Source metadata
Semantic Scholar, PubMed
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