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Mapping epitopes of U1-70K autoantibodies at single-amino acid resolution

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Taylor & Francis Group2016-01-20 更新2026-04-16 收录
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https://tandf.figshare.com/articles/dataset/Mapping_epitopes_of_U1_70K_autoantibodies_at_single_amino_acid_resolution/1626655/1
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The mechanisms underlying development of ribonucleoprotein (RNP) autoantibodies are unclear. The U1-70K protein is the predominant target of RNP autoantibodies, and the RNA binding domain has been shown to be the immunodominant autoantigenic region of U1-70K, although the specific epitopes are not known. To precisely map U1-70K epitopes, we developed silicon-based peptide microarrays with &gt;5700 features, corresponding to 843 unique peptides derived from the U1-70K protein. The microarrays feature overlapping peptides, with single-amino acid resolution in length and location, spanning amino acids 110–170 within the U1-70K RNA binding domain. We evaluated the serum IgG of a cohort of patients with systemic lupus erythematosus (SLE; <i>n</i> = 26) using the microarrays, and identified multiple reactive epitopes, including peptides 116–121 and 143–148. Indirect peptide ELISA analysis of the sera of patients with SLE (<i>n</i> = 88) revealed that ∼14% of patients had serum IgG reactivity to 116–121, while reactivity to 143–148 appeared to be limited to a single patient. SLE patients with serum reactivity to 116–121 had significantly lower SLE Disease Activity Index (SLEDAI) scores at the time of sampling, compared to non-reactive patients. Minimal reactivity to the peptides was observed in the sera of healthy controls (<i>n</i> = 92). Competitive ELISA showed antibodies to 116–121 bind a common epitope in U1-70K (68–72) and the matrix protein M1 of human influenza B viruses. Institutional Review Boards approved this study. Knowledge of the precise epitopes of U1-70K autoantibodies may provide insight into the mechanisms of development of anti-RNP, identify potential clinical biomarkers and inform ongoing clinical trails of peptide-based therapeutics.
提供机构:
Madoo Varma; Chih Long Liu
创建时间:
2015-12-23
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