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Belatacept confers increased patient and graft survival in renal transplant recipients relative to calcineurin inhibitors, but is associated with an increased rate of acute rejection. Recent immunophenotypic studies comparing pre-transplant T cell phenotypes of patients who reject vs. those that remain stable on belatacept identified three potential “risky” memory T cell subsets that potentially underlie belatacept-resistant rejection: CD4(+) CD28(+) T(EM), CD8(+) CD28(null) and CD4(+) CD57(+) PD1(−) subsets. Here, we compared key phenotypic and functional aspects of these human memory T cell subsets, with the goal of identifying additional potential targets to modulate them. Results demonstrate that TIGIT, an increasingly well-appreciated immune checkpoint receptor, was expressed on all three risky memory T cell subsets in vitro and in vivo in the presence of belatacept. Co-culture of human memory CD4(+) and CD8(+) T cells with an agonistic anti-TIGIT mAb significantly increased apoptotic cell death of all three risky memory T cell subsets. Mechanistically, TIGIT-mediated apoptosis of risky memory T cells was dependent on FOXP3(+) Treg, suggesting that agonism of the TIGIT pathway increases FOXP3(+) Treg suppression of human memory T cell populations. Overall, these data suggest that TIGIT agonism could represent a new therapeutic target to inhibit belatacept-resistant rejection during transplantation.

Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural-network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on data-sets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.