Back

Insulin B peptide-MHC class II-specific chimeric antigen 1 receptor-Tregs prevent autoimmune diabetes

Spanier, J. A.; Fung, V.; Wardell, C. M.; Alkhatib, M. H.; Chen, Y.; Swanson, L. A.; Dwyer, A. J.; Weno, M. E.; Silva, N.; Mitchell, J. S.; Orban, P. C.; Mojibian, M.; Verchere, C. B.; Fife, B. T.; Levings, M. K.

2023-02-24 immunology
10.1101/2023.02.23.529737 bioRxiv
Show abstract

Adoptive immunotherapy with Tregs is a promising approach for prevention or treatment of type 1 diabetes. Islet antigen-specific Tregs have more potent therapeutic effects than polyclonal cells, but their low frequency is a barrier for clinical application. To generate Tregs that recognize islet antigens, we engineered a chimeric antigen receptor (CAR) derived from a monoclonal antibody with specificity for the insulin B-chain 10-23 peptide presented in the context of the IAg7 MHC class II allele present in NOD mice. Peptide specificity of the resulting InsB-g7 CAR was confirmed by tetramer staining and T cell proliferation in response to recombinant or islet-derived peptide. The InsB-g7 CAR re-directed NOD Treg specificity such that insulin B 10-23-peptide stimulation enhanced suppressive function, measured via reduction of proliferation and IL-2 production by BDC2.5 T cells and CD80 and CD86 expression on dendritic cells. Co-transfer of InsB-g7 CAR Tregs prevented adoptive transfer diabetes by BDC2.5 T cells in immunodeficient NOD mice. In wild type NOD mice, InsB-g7 CAR Tregs stably expressed Foxp3 and prevented spontaneous diabetes. These results show that engineering Treg specificity for islet antigens using a T cell receptor-like CAR is a promising new therapeutic approach for the prevention of autoimmune diabetes. Brief SummaryChimeric antigen receptor Tregs specific for an insulin B-chain peptide presented by MHC class II prevent autoimmune diabetes.

Matching journals

The top 4 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.