HLA risk alleles cause Type 1 Diabetes by modifying the selection of T cell receptors that recognize pancreatic beta cell antigens

Project Summary Type 1 Diabetes (T1D) is a debilitating, lifelong disease which currently affects 20 million people worldwide. Multiple lines of evidence have shown that T cells target and destroy pancreatic β-cells in individuals with T1D. Though several key autoantigens have been identified, T cell antigen receptors (TCRs)

that recognize these antigens circulate in most individuals, including those who never get T1D. Genetic studies have shown that T1D is highly heritable, with HLA genes accounting for more risk than any other locus. We and others have shown that HLA variation influences which TCR sequences survive thymic selection. TCR sequence has a documented effect on T cell state: select V and J genes give rise to

mucosal-associated invariant T (MAIT) cells and hydrophobic CDR3β residues promote regulatory T cell (Treg) fate (Lagattuta et al., Nat. Immunol., 2022). Because each T cell’s TCR sequence is randomly acquired through V(D)J recombination prior to T cell differentiation, these relationships are likely causal. We propose

that any given TCR has a functional potential: a distinct likelihood of differentiation over all T cell states. The proposed research will test the hypothesis that HLA risk alleles cause T1D by selecting β-cell- specific TCRs with inflammatory functional potential. Such TCRs would promote the differentiation of β-

cell-specific T cells toward pathogenic, effector memory cell states, analogous to the way mucosal-associated invariant T cell (MAIT) TCRs drive acquisition of the MAIT transcriptional phenotype. Through rigorous statistical modeling, I will trace the impact of T1D HLA risk on β-cell-specific T cells: from HLA selection of TCR

patterns to the influence of TCR patterns on T cell state. Specifically, the study aims to (1) define which TCR sequence features influence T cell state, and (2) assess the impact of HLA risk alleles on the pancreatic β-cell specific TCR repertoire. The translational impact of robustly identifying β-cell-specific TCRs is clear: this could

help therapeutically target autoreactive T cells, could inform engineering of β-cell-specific Tregs, and would enable clinical monitoring of the β-cell-specific immune response longitudinally. More broadly, this work will provide a much-needed bioinformatic tool to cluster TCRs by antigen specificity, and will produce novel

insights into how TCR sequence features instruct T cell fate. The proposed training plan will enable me to: (A) develop skills in computational methods development, (B) cultivate statistical expertise for functional genomics and human genetics, (C) gain familiarity with the generation of high-throughput genomic data, (D) learn the immunological foundations and clinical

manifestations of autoimmune disease, and (E) sharpen professional scientific skills such as oral presentation and manuscript writing. A resource-rich training environment with close mentorship from experts in bioinformatic methods development, HLA genetics, TCR sequences, and T cell states in the context of

autoimmunity will prepare me to become a successful physician-scientist leader in computational immunology.