Dataset Information

High-throughput identification and dendritic cell-based functional validation of MHC class I-restricted Mycobacterium tuberculosis epitopes.

ABSTRACT: Emergence of drug-resistant strains of the pathogen Mycobacterium tuberculosis (Mtb) and the ineffectiveness of BCG in curtailing Mtb infection makes vaccine development for tuberculosis an important objective. Identifying immunogenic CD8+ T cell peptide epitopes is necessary for peptide-based vaccine strategies. We present a three-tiered strategy for identifying and validating immunogenic peptides: first, identify peptides that form stable complexes with class I MHC molecules; second, determine whether cytotoxic T lymphocytes (CTLs) raised against the whole protein antigen recognize and lyse target cells pulsed with peptides that passed step 1; third, determine whether peptides that passed step 2, when administered in vivo as a vaccine in HLA-A2 transgenic mice, elicit CTLs that lyse target cells expressing the whole protein antigen. Our innovative approach uses dendritic cells transfected with Mtb antigen-encoding mRNA to drive antigen expression. Using this strategy, we have identified five novel peptide epitopes from the Mtb proteins Apa, Mtb8.4 and Mtb19.

SUBMITTER: Nair SK

PROVIDER: S-EPMC4894389 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Publications

High-throughput identification and dendritic cell-based functional validation of MHC class I-restricted Mycobacterium tuberculosis epitopes.

Nair Smita K SK Tomaras Georgia D GD Sales Ana Paula AP Boczkowski David D Chan Cliburn C Plonk Kelly K Cai Yongting Y Dannull Jens J Kepler Thomas B TB Pruitt Scott K SK Weinhold Kent J KJ

Scientific reports 20140423

Emergence of drug-resistant strains of the pathogen Mycobacterium tuberculosis (Mtb) and the ineffectiveness of BCG in curtailing Mtb infection makes vaccine development for tuberculosis an important objective. Identifying immunogenic CD8+ T cell peptide epitopes is necessary for peptide-based vaccine strategies. We present a three-tiered strategy for identifying and validating immunogenic peptides: first, identify peptides that form stable complexes with class I MHC molecules; second, determine ...[more]

PMID: 24755960

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Project description:T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2Db and H-2Kb), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.

Dataset Information

High-throughput identification and dendritic cell-based functional validation of MHC class I-restricted Mycobacterium tuberculosis epitopes.

Publications

High-throughput identification and dendritic cell-based functional validation of MHC class I-restricted Mycobacterium tuberculosis epitopes.

Similar Datasets

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