Antigen discovery and specification of immunodominance hierarchies for MHCII-restricted epitopes. (November 2018) - Graham, Daniel; Luo, Chengwei; O'Connell, Daniel; Lefkovith, Ariel; Brown, Eric; Yassour, Moran; Varma, Mukund; Abelin, Jennifer; Conway, Kara; Jasso, Guadalupe; Matar, Caline; Carr, Steven; et al.
Abstract Identifying immunodominant T cell epitopes remains a significant challenge in the context of infectious disease, autoimmunity, and immuno-oncology. To address the challenge of antigen discovery, we developed a quantitative proteomic approach that enabled unbiased identification of major histocompatibility complex class II (MHCII)–associated peptide epitopes and biochemical features of antigenicity. On the basis of these data, we trained a deep neural network model for genome-scale predictions of immunodominant MHCII-restricted epitopes. We named this model bacteria originated T cell antigen (BOTA) predictor. In validation studies, BOTA accurately predicted novel CD4 T cell epitopes derived from the model pathogenListeria monocytogenes and the commensal microorganismMuribaculum intestinale . To conclusively define immunodominant T cell epitopes predicted by BOTA, we developed a high-throughput approach to screen DNA-encoded peptide–MHCII libraries for functional recognition by T cell receptors identified from single-cell RNA sequencing. Collectively, these studies provide a framework for defining the immunodominance landscape across a broad range of immune pathologies. A quantitative proteomic approach overcomes a major bottleneck in translational immunology, namely the identification of autologous and bacterial immunodominant major histocompatibility complex class II epitopes based on genomic sequences.