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.
