iTTCA-RF: a random forest predictor for tumor T cell antigens

Background Cancer is one of the most serious diseases threatening human health. Cancer immunotherapy represents the most promising treatment strategy due to its high efficacy and selectivity and lower side effects compared with traditional treatment. The identification of tumor T cell antigens is one of the most important tasks for antitumor vaccines development and molecular function investigation. Although several machine learning predictors have been developed to identify tumor T cell antigen, more accurate tumor T cell antigen identification by existing methodology is still challenging. Methods In this study, we used a non-redundant dataset of 592 tumor T cell antigens (positive samples) and 393 tumor T cell antigens (negative samples). Four types feature encoding methods have been studied to build an efficient predictor, including amino acid composition, global protein sequence descriptors and grouped amino acid and peptide composition. To improve the feature representation ability of the hybrid features, we further employed a two-step feature selection technique to search for the optimal feature subset. The final prediction model was constructed using random forest algorithm. Results Finally, the top 263 informative features were selected to train the random forest classifier for detecting tumor T cell antigen peptides. iTTCA-RF provides satisfactory performance, with balanced accuracy, specificity and sensitivity values of 83.71%, 78.73% and 88.69% over tenfold cross-validation as well as 73.14%, 62.67% and 83.61% over independent tests, respectively. The online prediction server was freely accessible at http://lab.malab.cn/~acy/iTTCA. Conclusions We have proven that the proposed predictor iTTCA-RF is superior to the other latest models, and will hopefully become an effective and useful tool for identifying tumor T cell antigens presented in the context of major histocompatibility complex class I.

Exchange catalysis by tapasin exploits conserved and allele-specific features of MHC-I molecules

The repertoire of peptides presented by major histocompatibility complex class I (MHC-I) molecules on the cell surface is tailored by the ER-resident peptide loading complex (PLC), which contains the exchange catalyst tapasin. Tapasin stabilizes MHC-I molecules and promotes the formation of stable peptide-MHC-I (pMHC-I) complexes that serve as T cell antigens. Exchange of suboptimal by high-affinity ligands is catalyzed by tapasin, but the underlying mechanism is still elusive. Here we analyze the tapasin-induced changes in MHC-I dynamics, and find the catalyst to exploit two essential features of MHC-I. First, tapasin recognizes a conserved allosteric site underneath the α2-1-helix of MHC-I, ‘loosening’ the MHC-I F-pocket region that accomodates the C-terminus of the peptide. Second, the scoop loop11–20 of tapasin relies on residue L18 to target the MHC-I F-pocket, enabling peptide exchange. Meanwhile, tapasin residue K16 plays an accessory role in catalysis of MHC-I allotypes bearing an acidic F-pocket. Thus, our results provide an explanation for the observed allele-specificity of catalyzed peptide exchange.

Hidden in Plain View: Discovery of Chimeric Diabetogenic CD4 T Cell Neo-Epitopes

The T cell antigens driving autoimmune Type 1 Diabetes (T1D) have been pursued for more than three decades. When diabetogenic CD4 T cell clones and their relevant MHCII antigen presenting alleles were first identified in rodents and humans, the path to discovering the peptide epitopes within pancreatic beta cell proteins seemed straightforward. However, as experimental results accumulated, definitive data were often absent or controversial. Work within the last decade has helped to clear up some of the controversy by demonstrating that a number of the important MHCII presented epitopes are not encoded in the natural beta cell proteins, but in fact are fusions between peptide fragments derived from the same or different proteins. Recently, the mechanism for generating these MHCII diabetogenic chimeric epitopes has been attributed to a form of reverse proteolysis, called transpeptidation, a process that has been well-documented in the production of MHCI presented epitopes. In this mini-review we summarize these data and their implications for T1D and other autoimmune responses.

TANTIGEN 2.0: a knowledge base of tumor T cell antigens and epitopes

We previously developed TANTIGEN, a comprehensive online database cataloging more than 1000 T cell epitopes and HLA ligands from 292 tumor antigens. In TANTIGEN 2.0, we significantly expanded coverage in both immune response targets (T cell epitopes and HLA ligands) and tumor antigens. It catalogs 4,296 antigen variants from 403 unique tumor antigens and more than 1500 T cell epitopes and HLA ligands. We also included neoantigens, a class of tumor antigens generated through mutations resulting in new amino acid sequences in tumor antigens. TANTIGEN 2.0 contains validated TCR sequences specific for cognate T cell epitopes and tumor antigen gene/mRNA/protein expression information in major human cancers extracted by Human Pathology Atlas. TANTIGEN 2.0 is a rich data resource for tumor antigens and their associated epitopes and neoepitopes. It hosts a set of tailored data analytics tools tightly integrated with the data to form meaningful analysis workflows. It is freely available at http://projects.met-hilab.org/tadb.

DNA origami demonstrate the unique stimulatory power of single pMHCs as T cell antigens

T cells detect with their T cell antigen receptors (TCRs) the presence of rare agonist peptide/MHC complexes (pMHCs) on the surface of antigen-presenting cells (APCs). How extracellular ligand binding triggers intracellular signaling is poorly understood, yet spatial antigen arrangement on the APC surface has been suggested to be a critical factor. To examine this, we engineered a biomimetic interface based on laterally mobile functionalized DNA origami platforms, which allow for nanoscale control over ligand distances without interfering with the cell-intrinsic dynamics of receptor clustering. When targeting TCRs via stably binding monovalent antibody fragments, we found the minimum signaling unit promoting efficient T cell activation to consist of two antibody-ligated TCRs within a distance of 20 nm. In contrast, transiently engaging antigenic pMHCs stimulated T cells robustly as well-isolated entities. These results identify pairs of antibody-bound TCRs as minimal receptor entities for effective TCR triggering yet validate the exceptional stimulatory potency of single isolated pMHC molecules.

Identification and characterization of antigen-specific CD4+ T cells targeting renally expressed antigens in human lupus nephritis with two independent methods

In the search for anti-renal autoreactivity in human lupus nephritis, we stimulated blood-derived CD4+ T cells from patients with systemic lupus erythematosus with various kidney lysates. Although only minor responses were detectable, these experiments led to the development of a search algorithm that combined autoantibody association with human lupus nephritis and target gene expression in inflamed kidneys. Applying this algorithm, five potential T cell antigens were identified. Blood-derived CD4+ T cells were then stimulated with these antigens. The cells were magnetically enriched prior to measurement with flow cytometry to facilitate the detection of very rare autoantigen-specific cells. The detected responses were dominated by IFN-γ-producing CD4+ T cells. Additionally, IL-10-producing CD4+ T cells were found. In a next step, T cell reactivity to each single antigen was independently evaluated with T cell libraries and [3H]-thymidine incorporation assays. Here, Vimentin and Annexin A2 were identified as the main T cell targets. Finally, Vimentin reactive T cells were also found in the urine of three patients with active disease. Overall, our experiments show that antigen-specific CD4+ T cells targeting renally expressed antigens arise in human lupus nephritis and correlate with disease activity and are mainly of the Th1 subset.