Back

Islet-antigen reactive B cells display a unique phenotype and BCR repertoire in autoantibody positive and recent-onset type 1 diabetes patients

Nicholas, C. A.; Tenson, F. A.; Evans, S. A.; Toole, K.; Broncucia, H.; Hesselberth, J. R.; Gottlieb, P. A.; Wells, K. L.; Smith, M. J.

2024-06-25 immunology
10.1101/2024.06.20.599914 bioRxiv
Show abstract

Autoreactive B cells play an important but ill-defined role in autoimmune type 1 diabetes (T1D). To better understand their contribution, we performed single cell gene and BCR-seq analysis on pancreatic islet antigen-reactive (IAR) B cells from the peripheral blood of nondiabetic (ND), autoantibody positive prediabetic (AAB), and recent-onset T1D individuals. We found that the frequency of IAR B cells was increased in AAB and T1D. IAR B cells from these donors had altered expression of B cell signaling, pro-inflammatory, infection, and antigen processing and presentation genes. Both AAB and T1D donors demonstrated a significant increase in certain heavy and light chain V genes, and these V genes were enriched in islet-reactivity. Public clones of IAR B cells were restricted almost entirely to AAB and T1D donors. IAR B cells were clonally expanded in the autoimmune donors, particularly the AAB group. Notably, a substantial fraction of IAR B cells in AAB and T1D donors appeared to be polyreactive, which was corroborated by analysis of recombinant monoclonal antibodies. These results expand our understanding of autoreactive B cell activation during T1D and identify unique BCR repertoire changes that may serve as biomarkers for increased disease risk. One Sentence SummaryPancreatic islet antigen-reactive B cells from individuals with prediabetes and recently diagnosed with type 1 diabetes display a unique phenotype and BCR repertoire compared to non-diabetic donors.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.