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Use of a glycomics array to establish the anti-carbohydrate antibody repertoire in type 1 diabetes

Author

Listed:
  • Paul M. H. Tran

    (Medical College of Georgia, Augusta University
    Yale School of Medicine)

  • Fran Dong

    (University of Colorado Denver, Mail Stop A-140)

  • Eileen Kim

    (Medical College of Georgia, Augusta University)

  • Katherine P. Richardson

    (Medical College of Georgia, Augusta University)

  • Lynn K. H. Tran

    (Medical College of Georgia, Augusta University)

  • Kathleen Waugh

    (University of Colorado Denver, Mail Stop A-140)

  • Diane Hopkins

    (Medical College of Georgia, Augusta University)

  • Richard D. Cummings

    (Beth Israel Deaconess Medical Center, Harvard Medical School)

  • Peng George Wang

    (Southern University of Science and Technology)

  • Marian J. Rewers

    (University of Colorado Denver, Mail Stop A-140)

  • Jin-Xiong She

    (Medical College of Georgia, Augusta University)

  • Sharad Purohit

    (Medical College of Georgia, Augusta University
    Medical College of Georgia, Augusta University
    College of Allied Health Sciences Augusta University)

Abstract

Type 1 diabetes (T1D) is an autoimmune disease, characterized by the presence of autoantibodies to protein and non-protein antigens. Here we report the identification of specific anti-carbohydrate antibodies (ACAs) that are associated with pathogenesis and progression to T1D. We compare circulatory levels of ACAs against 202 glycans in a cross-sectional cohort of T1D patients (n = 278) and healthy controls (n = 298), as well as in a longitudinal cohort (n = 112). We identify 11 clusters of ACAs associated with glycan function class. Clusters enriched for aminoglycosides, blood group A and B antigens, glycolipids, ganglio-series, and O-linked glycans are associated with progression to T1D. ACAs against gentamicin and its related structures, G418 and sisomicin, are also associated with islet autoimmunity. ACAs improve discrimination of T1D status of individuals over a model with only clinical variables and are potential biomarkers for T1D.

Suggested Citation

  • Paul M. H. Tran & Fran Dong & Eileen Kim & Katherine P. Richardson & Lynn K. H. Tran & Kathleen Waugh & Diane Hopkins & Richard D. Cummings & Peng George Wang & Marian J. Rewers & Jin-Xiong She & Shar, 2022. "Use of a glycomics array to establish the anti-carbohydrate antibody repertoire in type 1 diabetes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34341-2
    DOI: 10.1038/s41467-022-34341-2
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    References listed on IDEAS

    as
    1. Sharad Purohit & Tiehai Li & Wanyi Guan & Xuezheng Song & Jing Song & Yanna Tian & Lei Li & Ashok Sharma & Boying Dun & David Mysona & Sharad Ghamande & Bunja Rungruang & Richard D. Cummings & Peng Ge, 2018. "Multiplex glycan bead array for high throughput and high content analyses of glycan binding proteins," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    2. Andrew C. Tolonen & Nicholas Beauchemin & Charlie Bayne & Lingyao Li & Jie Tan & Jackson Lee & Brian M. Meehan & Jeffrey Meisner & Yves Millet & Gabrielle LeBlanc & Robert Kottler & Erdmann Rapp & Chr, 2022. "Synthetic glycans control gut microbiome structure and mitigate colitis in mice," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Joshua Chiou & Ryan J. Geusz & Mei-Lin Okino & Jee Yun Han & Michael Miller & Rebecca Melton & Elisha Beebe & Paola Benaglio & Serina Huang & Katha Korgaonkar & Sandra Heller & Alexander Kleger & Seba, 2021. "Interpreting type 1 diabetes risk with genetics and single-cell epigenomics," Nature, Nature, vol. 594(7863), pages 398-402, June.
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