Increased β-cell proliferation prior to immune-cell invasion prevents progression of type 1 diabetes

Type 1 diabetes (T1D) is characterized by pancreatic islet infiltration by autoreactive immune cells and a near-total loss of β-cells. Restoration of insulin-producing β-cells coupled with immunomodulation to suppress the autoimmune attack has emerged as a potential approach to counter T1D. Here we report that enhancing β-cell mass in female NOD mice early in life (prior to weaning) results in immunomodulation of T-cells, reduced islet infiltration and lower β-cell apoptosis, that together protect them from developing T1D. We observed that a model exhibiting β-cell hyperplasia on the NOD background (NOD-LIRKO) displayed altered β-cell antigens, and islet transplantation studies showed prolonged graft survival of NOD-LIRKO islets even upon exposure to diabetogenic splenocytes in vivo. Adoptive transfer of splenocytes from the NOD-LIRKOs prevented diabetes development in pre-diabetic NOD mice, while conversely, similar protective outcomes were obtained when NOD-LIRKO splenocytes were adoptively transferred after mixing them with diabetogenic NOD splenocytes in a dose-dependent manner. A significant increase in the splenic CD4+CD25+FoxP3+ regulatory T-cell (Treg) population in the NOD-LIRKO mice was observed to drive the protected phenotype since Treg depletion rendered NOD-LIRKO mice diabetic. The increase in Tregs coupled with a downregulation of key mediators of cellular function, upregulation of apoptosis and activation of TGF-β/SMAD3 signaling pathway in pathogenic T-cells favored reduced ability to kill β-cells. These data provide novel evidence that initiating β-cell proliferation, alone, prior to islet infiltration by immune cells alters the identity of β-cells, decreases pathologic self-reactivity of effector cells and increases Tregs to prevent progression of T1D. Microarray was performed by the Molecular Genetic Core Facility at Children’s Hospital Boston. CD8+ T-cells and Treg from both NOD-Lox and NOD-LIRKO at eight and twelve weeks of age were analyzed. Briefly, the microarray data was analyzed by correcting for background and normalizing, and estimating empirical array quality weights. We performed differential expression of the microarray data using the R package limma. Analysis was performed in R/Bioconductor.