Tumor Antigens and T-Cell Antigen Recognition Our research focuses on T-cell recognition of antigenic MHC-restricted peptides. Our model system is a xenoreactive murine CD8+ T cell clone (AH3) that recognizes both human HLA-A2 and murine H2- Db class I MHC when associated with two different peptides. We have described three key differences in the AH3 response to the two MHC targets: 1) the AH3 TCR binds more tightly to HLA-A2 than H2-Db molecules, 2) the AH3 response to HLA-A2 does not require CD8 (CD8-independent), while CD8 is required in the response to H2-Db (CD8 dependent), 3) stimulation of AH3 T cells with the A2 and Db MHC targets results in similar proliferative and cytolytic responses, while only stimulation with Db (and not A2) produces pro-inflammatory cytokines (IFNγ, TNFalpha). Our first priority has been to establish the AH3 TCR transgenic mouse colony. The AH3 mouse was crossed onto the RAG-/- background so that the majority of peripheral T cells would express the AH3 TCR. However, loss of RAG function leaves the mice immunodeficient, and thus they must be maintained in a barrier facility. We have bred several generations of AH3 mice at our NCI mice facility without encountering any problem. Previously we determined the proliferative, cytolytic, and cytokine secretion profiles of AH3 T cells stimulated with A2 and Db targets bound to their cognate peptides, and several peptide variants. However, these studies were done using a chronically in vitro-stimulated AH3 clone, which has several disadvantages compared to using freshly isolated T cells from TCR transgenic mice. Therefore, we are repeating these experiments using cells from the AH3 mice and we have done preliminary experiments to optimize effector and target cell numbers, and peptide concentrations, and we anticipate completion of these studies against a panel of peptide variants very soon. In order to better understand the biochemical interaction of the AH3 TCR with its cognative peptides, p1049/A2 and p1058/Db, we have examined the binding of the complexes by surface plasmon resonance. We have completed our analysis of binding to a panel of 17 p1049 variants, and three p1058 variants. We will attempt to correlate affinities and on/off rates with the biological response of AH3 to each peptide variant. We are using flow cytometry and gene microarray tools to examine the signalling pathways utilized by AH3 T cells as they respond to A2 (CD8 independent) and Db (CD8 dependent) targets. For both approaches, T cells were isolated from AH3 mice, and stimulated with MHC tetramers corresponding to A2 plus cognate or irrelevant peptide, and Db plus cognate or irrelevant peptide. Fluorescent cell barcoding is a powerful tool to analyze the phenotype of cells that receive multiple different treatments. The technique takes advantage of current multi-laser multi-detector flow cytometers, and may be used to collect information about the presence, absence, and phosphorylation state of numerous intermediates within signalling pathways. Briefly, AH3 T cells are treated separately with tetramer as described, then stained with unique identifiers (barcoded). The treatment groups are then pooled, and stained for signalling intermediates, then analyzed by flow cytometry. Successful barcoding requires extensive optimization of the staining reagents. We have recently completed this optimization, and can now simulatneously assess 11 different signalling intermediates representing three major T cell signalling pathways. For gene expression arrays, AH3 T cells were stimulated with MHC tetramers as described above. At specific time points after stimulation, the T cells were lysed and total RNA was isolated. Total RNA was then converted to cDNA, linearly amplified, and analyzed by hybridization to Illumina Whole Genome Expression arrays.
The second aim of our study involves T-cell recognition of wt p53 peptide epitopes in cancer. Three HLA-A2.1-restricted, CTL-defined wild type p53 epitopes have been identified for use in cancer vaccines; they are p5365-73, p53149-157, and p53264-272. Our results indicated, however, that CTL responses to these epitope is generated among only a few normal donors and cancer patients tested. As CD4+ T helper-defined epitopes have been shown to enhance vaccine responses, we have characterized CD4+ T helper cell-defined wt p53 for vaccine use. This will result in better applicability of a peptide-based vaccine. CD4+ regulatory T cells (T-regs), which produce the immunosuppressive IL-10 cytokine, contribute to immune tolerance as well as cancer progression. Identifying and eliminating these cells would enhance the efficacy of cancer immunotherapy, in particular, therapy targeting self tumor antigens, such as p53. Tumor specific-T-reg have been shown to recognize epitopes that tend to overlap with those also recognized by CD4+ T helper cells. We tested a panel of known CD4+ T cell-defined p53 epitopes for their ability to enhance IL-10 production from PBMC of normal donors and have tentatively identified the p5325-35 peptide as a p53-specific T-reg peptide. In the final part of our study, we addressed the role of Wip1 in innate immunity through the use of a Wip1-deficient mouse model. These mice were viable, but showed a variety of postnatal abnormalities. Notably, mice lacking Wip1 showed increased susceptibility to pathogens and diminished T- and B-cell functionality. Although it has been demonstrated that Wip1 is associated with immune responses, the role of Wip1 in Toll-Like-Receptor-mediated immune responses has not been defined. Recently, we have showed that Wip1 plays a critical role in the innate immunity by regulating the expression of cytokines through modulation of STAT3 activity. We also showed that STAT3 is one of the substrates of Wip1 by demonstrating its dephosphorylation of STAT3 serine 691 in vitro. Our data suggests that STAT3 serine 691 is an important phosphorylation site for activation of STAT3 upon LPS stimulation in macrophages. Therefore, this study demonstrates that Wip1 is a novel regulator of innate immunity.
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