Deep analysis of epitope specific CD4+ T cells before the onset of T1D

About This Grant

PROJECT SUMMARY/ABSTRACT Type 1 diabetes (T1D) is an immune-mediated disease in which functional β cells are lost, leading to insulin dependence. HLA class II genotypes are the dominant genetic risk factor, suggesting a key role for CD4+ T cells. Islet-reactive T cells produce inflammatory cytokines and support formation and affinity maturation autoantibodies, the appearance of which accurately predicts disease risk. At-risk individuals develop additional specificities during progression. Therefore, epitope occurs and may be an indicator of impending disease onset. Our preliminary studies show that expanded numbers of CD4+ T cells that target self-antigens are present in individuals with established T1D. Specifically, our preliminary data enabled us to define an array of self-epitopes that are disease relevant and recognized in individuals with two of the highest risk HLA class II genotypes. These include newly discovered conventional and neoepitopes from GAD65 and other beta cell antigens. It is believed that self-reactive T cell responses evolve over time and that beta cell injury elicits neoepitope formation. However, these phenomena have not been directly studied, leaving a significant knowledge gap. Given their important role in orchestrating autoimmunity, understanding the CD4+ T cell responses is a topic of great importance. Indeed, the approval of teplizumab as a therapy to delay the progression of T1D increases the rationale for understanding and monitoring epitope spreading in at-risk individuals. Our work will test the hypothesis that in at-risk subjects there are pivotal shifts in the number, phenotype, and transcriptional programs of autoreactive CD4+ T cells and an expansion of distinct TCR sequences and that these changes reveal important pathways that drive disease progression. Our first aim will characterize longitudinal changes in the number and phenotype of autoreactive T cells in at-risk subjects by performing high dimensional flow cytometry analysis using a robust surface marker panel combined with second generation HLA class II tetramer reagents. Our second aim will utilize the 10X genomics platform to generate rich whole genome RNAseq data sets on antigen specific T cells sorted from IAA positive at-risk individuals. This will allow deep molecular profiling of autoreactive T cells to identify immune cell subsets that contribute to T1D progression and deep analysis of TCR sequences and their structural features, tracking their phenotype trajectories over time and the emergence of key T cell transcriptional states in at-risk subjects. In total, our proposed studies will advance our understanding of T1D, providing important new insights about the T cell specificities, phenotypes, and functional attributes that drive the development of T1D the immunologic changes and mechanisms that promote autoimmune progression.