We investigate the accumulated microbial and autoantigen antibody repertoire in adult-onset dermatomyositis patients sero-positive for TIF1γ (TRIM33) autoantibodies. We use an untargeted high-throughput approach which combines immunoglobulin disease-specific epitope-enrichment and identification of microbial and human antigens. We observe antibodies recognizing a wider repertoire of microbial antigens in dermatomyositis. Antibodies recognizing viruses and Poxviridae family species are significantly enriched. The identified autoantibodies recognise a large portion of the human proteome, including interferon regulated proteins; these proteins cluster in specific biological processes. In addition to TRIM33, we identify autoantibodies against eleven further TRIM proteins, including TRIM21. Some of these TRIM proteins share epitope homology with specific viral species including poxviruses. Our data suggest antibody accumulation in dermatomyositis against an expanded diversity of microbial and human proteins and evidence of non-random targeting of specific signalling pathways. Our findings indicate that molecular mimicry and epitope spreading events may play a role in dermatomyositis pathogenesis. Megremis, Walker at al. identify immunogenic epitopes in dermatomyositis patients. They identify antibodies recognizing a wider diversity of microbial antigens including poxviruses, and autoantibodies recognizing a large portion of the human proteome. Shared epitope homology between viral and human proteins suggests that molecular mimicry and epitope spreading events may play a role in dermatomyositis pathogenesis.
Analysis of human total antibody repertoires in TIF1γ autoantibody positive dermatomyositis
Spyridon Megremis,Thomas D. J. Walker,Xiaotong He,J. O’Sullivan,W. Ollier,H. Chinoy,N. Pendleton,A. Payton,L. Hampson,I. Hampson,J. Lamb
Published 2021 in Communications Biology
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- Publication year
2021
- Venue
Communications Biology
- Publication date
2021-03-26
- Fields of study
Biology, Medicine
- Identifiers
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- Source metadata
Semantic Scholar, PubMed
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