MHC-based detection of antigen-specific CD8+ T cell responses

The hallmark of adaptive immunity is its ability to recognise a wide range of antigens and technologies that capture this diversity are therefore of substantial interest. New methods have recently been developed that allow the parallel analysis of T cell reactivity against vast numbers of different epitopes in limited biological material. These technologies are based on the joint binding of differentially labelled MHC multimers on the T cell surface, thereby providing each antigen-specific T cell population with a unique multicolour code. This strategy of ‘combinatorial encoding’ enables detection of many (at least 25) different T cell populations per sample and should be of broad value for both T cell epitope identification and immunomonitoring

A Broad Profile of Co-Dominant Epitopes Shapes the Peripheral Mycobacterium tuberculosis Specific CD8+ T-Cell Immune Response in South African Patients with Active Tuberculosis.

We studied major histocompatibility complex (MHC) class I peptide-presentation and nature of the antigen-specific CD8+ T-cell response from South African tuberculosis (TB) patients with active TB. 361 MHC class I binding epitopes were identified from three immunogenic TB proteins (ESAT-6 [Rv3875], Ag85B [Rv1886c], and TB10.4 [Rv0288], including amino acid variations for Rv0288, i.e., A10T, G13D, S27N, and A71S for MHC allotypes common in a South African population (e.g., human leukocyte antigen [HLA]-A*30, B*58, and C*07). Inter-allelic differences were identified regarding the broadness of the peptide-binding capacity. Mapping of frequencies of Mycobacterium tuberculosis (M. tb) antigen-specific CD8+ T-cells using 48 different multimers, including the newly constructed recombinant MHC class I alleles HLA-B*58:01 and C*0701, revealed a low frequency of CD8+ T-cell responses directed against a broad panel of co-dominant M. tb epitopes in the peripheral circulation of most patients. The antigen-specific responses were dominated by CD8+ T-cells with a precursor-like phenotype (CD45RA+CCR7+). The data show that the CD8+ T-cell response from patients with pulmonary TB (prior to treatment) is directed against subdominant epitopes derived from secreted and non-secreted M. tb antigens and that variant, natural occurring M. tb Rv0288 ligands, have a profound impact on T-cell recognition

Identification of a naturally processed HLA-A*02:01-restricted CTL epitope from the human tumor-associated antigen Nectin-4.

Nectin-4 is a tumor antigen present on the surface of breast, ovarian and lung carcinoma cells. It is rarely present in normal adult tissues and is therefore a candidate target for cancer immunotherapy. Here, we identified a Nectin-4 antigenic peptide that is naturally presented to T cells by HLA-A2 molecules. We first screened the 502 nonamer peptides of Nectin-4 (510 amino acids) for binding to and off-rate from eight different HLA class I molecules. We then combined biochemical, cellular and algorithmic assays to select 5 Nectin-4 peptides that bound to HLA-A*02:01 molecules. Cytolytic T lymphocytes were obtained from healthy donors, that specifically lyzed HLA-A2(+) cells pulsed with 2 out of the 5 peptides, indicating the presence of anti-Nectin-4 CD8(+) T lymphocytes in the human T cell repertoire. Finally, an HLA-A2-restricted cytolytic T cell clone derived from a breast cancer patient recognized peptide Nectin-4145-153 (VLVPPLPSL) and lyzed HLA-A2(+) Nectin-4(+) breast carcinoma cells. These results indicate that peptide Nectin-4145-153 is naturally processed for recognition by T cells on HLA-A2 molecules. It could be used to monitor antitumor T cell responses or to immunize breast cancer patients

Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes

Background: T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots. Results: Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and Support Vector Machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the formulation of an epitope-based vaccine that could be effective against multiple variants of the pathogen and applicable to a large proportion of the human population. Conclusion: Hotspot Hunter is publicly accessible at http://antigen.i2r.a-star.edu.sg/hh/. It is a new generation computational tool aiding in epitope-based vaccine design