Characterization of the peptide binding specificity of the HLA class I alleles B*38:01 and B*39:06

Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes

A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machine learning methods. Despite the recent advances made in generating high-quality MHC-eluted, naturally processed ligandome, the reliability of new predictors on these epitopes has yet to be evaluated. This study reports the latest benchmarking on an extensive set of MHC-binding predictors by using newly available, untested data of both synthetic and naturally processed epitopes. 32 human leukocyte antigen (HLA) class I and 24 HLA class II alleles are included in the blind test set. Artificial neural network (ANN)-based approaches demonstrated better performance than regression-based machine learning and structural modeling. Among the 18 predictors benchmarked, ANN-based mhcflurry and nn_align perform the best for MHC class I 9-mer and class II 15-mer predictions, respectively, on binding/non-binding classification (Area Under Curves = 0.911). NetMHCpan4 also demonstrated comparable predictive power. Our customization of mhcflurry to a pan-HLA predictor has achieved similar accuracy to NetMHCpan. The overall accuracy of these methods are comparable between 9-mer and 10-mer testing data. However, the top methods deliver low correlations between the predicted versus the experimental affinities for strong MHC binders. When used on naturally processed MHC-ligands, tools that have been trained on elution data (NetMHCpan4 and MixMHCpred) shows better accuracy than pure binding affinity predictor. The variability of false prediction rate is considerable among HLA types and datasets. Finally, structure-based predictor of Rosetta FlexPepDock is less optimal compared to the machine learning approaches. With our benchmarking of MHC-binding and MHC-elution predictors using a comprehensive metrics, a unbiased view for establishing best practice of T-cell epitope predictions is presented, facilitating future development of methods in immunogenomics.

HLA-B*39:06 Efficiently Mediates Type 1 Diabetes in a Mouse Model Incorporating Reduced Thymic Insulin Expression

Type 1 diabetes (T1D) is characterized by T cell-mediated destruction of the insulin-producing β cells of the pancreatic islets. Among the loci associated with T1D risk, those most predisposing are found in the MHC region. HLA-B*39:06 is the most predisposing class I MHC allele and is associated with an early age of onset. To establish an NOD mouse model for the study of HLA-B*39:06, we expressed it in the absence of murine class I MHC. HLA-B*39:06 was able to mediate the development of CD8 T cells, support lymphocytic infiltration of the islets, and confer T1D susceptibility. Because reduced thymic insulin expression is associated with impaired immunological tolerance to insulin and increased T1D risk in patients, we incorporated this in our model as well, finding that HLA-B*39:06-transgenic NOD mice with reduced thymic insulin expression have an earlier age of disease onset and a higher overall prevalence as compared with littermates with typical thymic insulin expression. This was despite virtually indistinguishable blood insulin levels, T cell subset percentages, and TCR Vβ family usage, confirming that reduced thymic insulin expression does not impact T cell development on a global scale. Rather, it will facilitate the thymic escape of insulin-reactive HLA-B*39:06-restricted T cells, which participate in β cell destruction. We also found that in mice expressing either HLA-B*39:06 or HLA-A*02:01 in the absence of murine class I MHC, HLA transgene identity alters TCR Vβ usage by CD8 T cells, demonstrating that some TCR Vβ families have a preference for particular class I MHC alleles.

Protein nanovaccine confers robust immunity against Toxoplasma

We designed and produced a self-assembling protein nanoparticle. This self-assembling protein nanoparticle contains five CD8+ HLA-A03-11 supertypes-restricted epitopes from antigens expressed during Toxoplasma gondii's lifecycle, the universal CD4+ T cell epitope PADRE, and flagellin as a scaffold and TLR5 agonist. These CD8+ T cell epitopes were separated by N/KAAA spacers and optimized for proteasomal cleavage. Self-assembling protein nanoparticle adjuvanted with TLR4 ligand-emulsion GLA-SE were evaluated for their efficacy in inducing IFN-γ responses and protection of HLA-A*1101 transgenic mice against T. gondii. Immunization, using self-assembling protein nanoparticle-GLA-SE, activated CD8+ T cells to produce IFN-γ. Self-assembling protein nanoparticle-GLA-SE also protected HLA-A*1101 transgenic mice against subsequent challenge with Type II parasites. Hence, combining CD8+ T cell-eliciting peptides and PADRE into a multi-epitope protein that forms a nanoparticle, administered with GLA-SE, leads to efficient presentation by major histocompatibility complex Class I and II molecules. Furthermore, these results suggest that activation of TLR4 and TLR5 could be useful for development of vaccines that elicit T cells to prevent toxoplasmosis in humans.