Rheumatoid arthritis (RA) can be initiated and driven by immune responses to multiple antigenic epitopes including those in cartilage proteoglycan (PG, aggrecan) and type II collagen. RA is driven by T helper 1 (Th1) or Th17 pro-inflammatory T cell responses. LEAPS (Ligand Epitope Antigen Presentation System) DerG peptide conjugate vaccines were prepared using epitopes from PG that elicit immune responses in RA patients: epitope PG70 (DerG-PG70, also designated CEL-4000) and the citrullinated form of another epitope (PG275Cit). The LEAPS peptides were administered alone or together in Seppic ISA51vg adjuvant to mice with PG G1 domain-induced arthritis (GIA), a mouse model of RA. Each of these LEAPS peptides and the combination modulated the inflammatory response and stopped the progression of arthritis in the GIA mouse model. Despite having a therapeutic effect, the DerG-PG275Cit vaccine did not elicit significant antibody responses, whereas DerG-PG70 (alone or with DerG-PG275Cit) induced both therapy and antibodies. Spleen T cells from GIA mice, vaccinated with the DerG LEAPS peptides, preferentially produced anti-inflammatory (IL-4 and IL-10) rather than pro-inflammatory (IFN-γ or IL-17) cytokines in culture. Similarly, cytokines secreted by CD4+ cells of unvaccinated GIA mice, differentiated in vitro to Th2 cells and treated with either or both DerG vaccine peptides, exhibited an anti-inflammatory (IL-4, IL-10) profile. These results suggest that the two peptides elicit different therapeutic immune responses by the immunomodulation of disease-promoting pro-inflammatory responses and that the combination of the two LEAPS conjugates may provide broader epitope coverage and, in some cases, greater efficacy than either conjugate alone.
Vaccination by Two DerG LEAPS Conjugates Incorporating Distinct Proteoglycan (PG, Aggrecan) Epitopes Provides Therapy by Different Immune Mechanisms in a Mouse Model of Rheumatoid Arthritis
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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